{"as_of":"2026-08-10T00:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:916af0a53b9171e7385e7bdf0937f469d93d46b4210b0571d421758448a937d3","coverage":[{"denominator":66,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":66,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:07:32.778533Z","state":"measured"},{"denominator":66,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":66,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.01309/citation-record","integrity":"/paper/2507.01309/integrity","json":"/paper/2507.01309/citation-record.json","paper":"/paper/2507.01309"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:38.194477Z","title":"Fused-layer cnn accelerators,","venue":null,"work_id":"83a25786-91eb-4f72-a7f3-01a037b154ba","year":2016},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.248689Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:33dd564998b885ce947b69b7b79d10910d121c12d6fddd607c02699850914b91","observation_id":"a4363b93-9129-4fce-9fcf-7c4464c5e5f0","resolution":{"observed_at":"2026-08-06T21:07:38.272553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:38.057010Z","title":"Diannao: A small-footprint high-throughput accelerator for ubiqui- tous machine-learning,","venue":null,"work_id":"7b727857-5c5b-4840-aa54-4fbe01b1af12","year":2014},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.283720Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:4b11847c4a45ff8f82fb4a02e38862a5c89170f98b520d07cf60be6196c52c8b","observation_id":"56350b75-c8c8-4ced-804e-7d88be3c7cb6","resolution":{"observed_at":"2026-08-06T21:07:38.119743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:37.921958Z","title":"Eyeriss: An energy- efficient reconfigurable accelerator for deep convolutional neural net- works,","venue":null,"work_id":"b930da91-80ec-4b3f-b921-df54efc55bc5","year":2016},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.343080Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:685ace9bdc08ced55a2f52dd882f5adb3fb7455ec2e38277285f042d0a3044ff","observation_id":"aecab43f-e06f-4903-bc06-0913ea7b790c","resolution":{"observed_at":"2026-08-06T21:07:37.967686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:27.410156Z","title":"Eyeriss v2: A flexible accelerator for emerging deep neural networks on mobile devices,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.410156Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:104053f3b3ae584e9b68d0501268367ee2cce46c203c6e23034e46855b5f8a95","observation_id":"fcd6e8b5-4baa-457c-a660-4b9a6c5634ec","resolution":{"observed_at":"2026-08-06T21:07:27.410156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:37.796999Z","title":"A 28.6 mj/iter stable diffusion processor for text-to-image generation with patch similarity-based sparsity augmentation and text-based mixed-precision,","venue":null,"work_id":"a0c566a4-512d-47ce-bf0f-edeb768958f6","year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.460154Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:1a8ed793867eb3b8b408d3b9a696b9a35c71031738c32f783b4b72f4ddccfc86","observation_id":"3a836484-a75d-4e04-bd51-1a471340f212","resolution":{"observed_at":"2026-08-06T21:07:37.855754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:37.642574Z","title":"Flashattention-2: Faster attention with better parallelism and work partitioning,","venue":null,"work_id":"a07e1913-6b92-4ba1-a2e5-ac1eacd90832","year":2023},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.506034Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:6cd3cdfa5e3bac79426b777489afcd0f57b85a26ed1eae94ef5e66513d7021d4","observation_id":"36de3f75-f31c-4a47-a410-2b47bb853a07","resolution":{"observed_at":"2026-08-06T21:07:37.717251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.14135","last_updated":"2022-06-23T17:53:32Z","snapshot_observed_at":"2026-07-06T13:14:48.753329Z","submitted_at":"2022-05-27T17:53:09Z","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.14135","snapshot_observed_at":"2026-08-06T21:07:27.571220Z","title":"Flashattention: Fast and memory-efficient exact attention with io-awareness,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.571220Z"},"links":{"cited_paper":"/paper/2205.14135","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:1e5abe40eee42ad45842e27478a69dd51b76c26fecabd92e281ab9ee7768e5bc","observation_id":"575e913b-76d3-4021-81dc-e68f900ac823","resolution":{"observed_at":"2026-08-06T21:07:27.571220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-06T21:07:27.599680Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.599680Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:3c93f25a0ded60d3cceb54fdc31e2420b32e3f9150d1c447f4dfd5f186b33da4","observation_id":"ddbcf4ac-bd3d-48a5-b010-4e0e130a3952","resolution":{"observed_at":"2026-08-06T21:07:27.599680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:37.534781Z","title":"Adaptable butterfly accelerator for attention-based nns via hardware and algorithm co-design,","venue":null,"work_id":"0df4dd5d-d816-4974-9ad6-78be26281973","year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.647853Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:e5f83abf3a870e2c5c750bc02f2cb6e9b0046f1a1e524b9d2ffb0eea6445ca61","observation_id":"5c66f929-38f2-42d8-ba64-8e56d536a806","resolution":{"observed_at":"2026-08-06T21:07:37.570101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:37.419341Z","title":"Structural pruning for diffusion models,","venue":null,"work_id":"29ec581b-4d52-40f6-9b95-ebbbad0c0ffc","year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.695739Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:91cce6e7051a7b838999bdeedb91966b3ae16c102638532530003a96b2f272d9","observation_id":"f3d55eb2-32a4-47f4-a584-7c935bd4df1d","resolution":{"observed_at":"2026-08-06T21:07:37.494306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:37.246939Z","title":"Gemmini: Enabling systematic deep- learning architecture evaluation via full-stack integration,","venue":null,"work_id":"eca99b05-dffa-440c-ab7e-ab098e5301dd","year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.753096Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:a689213ae7bc2407169d991eed7a473b815d794742f4989b51b4e63eec9368fb","observation_id":"9ff26373-5b9a-49e7-a5b4-9177074c8984","resolution":{"observed_at":"2026-08-06T21:07:37.357689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:37.127000Z","title":"Aˆ 3: Accelerating attention mechanisms in neural networks with approximation,","venue":null,"work_id":"3dfb5d54-ca67-4b31-81e5-44c59258bcc1","year":2020},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.799457Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:b623a683aef79908fa29f11f5f8168bdf50a9b9dc402ad095e2ab080e5191378","observation_id":"276f4439-0060-4ac8-8e0c-6845d8ea7620","resolution":{"observed_at":"2026-08-06T21:07:37.182595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:36.940653Z","title":"A k-means clustering algorithm,","venue":null,"work_id":"0a7c11bf-b4c4-4282-8658-3b01e3483b44","year":1979},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.842168Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:1332aaf0a2b33868ff8e956cd93420e8812c08956152520a26fe17071944ad59","observation_id":"8ad32429-d46a-463d-a353-94f079618426","resolution":{"observed_at":"2026-08-06T21:07:37.045436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:27.890462Z","title":"Ptqd: Accurate post-training quantization for diffusion models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.890462Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:25c6e416356e43988ed853032263cbdaf44924f1675954d1f21932423985efdd","observation_id":"ea31d8cf-6a77-42d3-8d41-9178cf97b718","resolution":{"observed_at":"2026-08-06T21:07:27.890462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.08415","last_updated":"2023-06-06T01:53:32Z","snapshot_observed_at":"2026-07-06T05:01:27.910364Z","submitted_at":"2016-06-27T19:20:40Z","title":"Gaussian Error Linear Units (GELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.08415","snapshot_observed_at":"2026-08-06T21:07:27.941121Z","title":"Gaussian error linear units (gelus),","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.941121Z"},"links":{"cited_paper":"/paper/1606.08415","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:0973b16a2eaf9dd65c9bd6ccf30d2eeea009e425ebaff251126db0932fcb587a","observation_id":"cc9fb713-6b89-40dc-a72b-179006c7ab74","resolution":{"observed_at":"2026-08-06T21:07:27.941121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:27.983517Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:27.983517Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:06c466a6a5c4a3d8f413da54d0308bc20515ed75a63537755a6ac279f7c7058d","observation_id":"11fd366c-ed41-4434-8e0d-1c54fd8595e4","resolution":{"observed_at":"2026-08-06T21:07:27.983517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01282","last_updated":"2024-01-05T12:41:13Z","snapshot_observed_at":"2026-07-06T16:42:12.176043Z","submitted_at":"2023-11-02T14:57:03Z","title":"FlashDecoding++: Faster Large Language Model Inference on GPUs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01282","snapshot_observed_at":"2026-08-06T21:07:28.045319Z","title":"Flashdecoding++: Faster large language model inference on gpus,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.045319Z"},"links":{"cited_paper":"/paper/2311.01282","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:f5624f6b8984fd5e6ee2b0375416b06297349e858daa552f5fbc88012dcddaa2","observation_id":"a4678885-969e-4a5f-86b7-82f6600a50de","resolution":{"observed_at":"2026-08-06T21:07:28.045319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:28.140596Z","title":"In-datacenter performance analysis of a tensor processing unit,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.140596Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:280055103385a99d4400c64572da13687cfbd577e8ce7452c909f8541c3cfefd","observation_id":"577b58f7-16db-44fb-a0af-764c05bc2fa4","resolution":{"observed_at":"2026-08-06T21:07:28.140596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:36.806258Z","title":"Stripes: Bit-serial deep neural network computing,","venue":null,"work_id":"f5af8063-a6d6-4a24-bb91-c3a7e3660cd4","year":2016},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.164203Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:12b359c7bb4116f78417f4df27360f1a8e9e081cdf8f6d48e74e1febc033bcf2","observation_id":"bf274c72-d33c-44f7-b794-74bc6ede3a64","resolution":{"observed_at":"2026-08-06T21:07:36.874529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:28.296016Z","title":"Flat: An optimized dataflow for mitigating attention bottlenecks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.296016Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:c34964150037976505fd9592fd03e14160d78d3b43cf0e95c56ceff7cfa057fa","observation_id":"20c5ebdf-26f1-40ec-a68d-3eb498b13374","resolution":{"observed_at":"2026-08-06T21:07:28.296016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:28.353388Z","title":"Elucidating the design space of diffusion-based generative models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.353388Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:b0ba6a4270a946a5cd706139179bb2a9e7498c9016a9de05d3841b0b0d9e0ed6","observation_id":"c1f2bbf0-bf93-458a-98eb-30b67d943c51","resolution":{"observed_at":"2026-08-06T21:07:28.353388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:36.651199Z","title":"Bk-sdm: Archi- tecturally compressed stable diffusion for efficient text-to-image gen- eration,","venue":null,"work_id":"b94f2a50-96d5-4ed5-8cdf-31a99024ce20","year":2023},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.386777Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:4453622aa088ee45fb39108f3814e56575e823fbe6ea52a602cc50787fa69263","observation_id":"fdd9e08e-fda2-4cfd-9b37-4049fc632419","resolution":{"observed_at":"2026-08-06T21:07:36.707389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.01321","last_updated":"2021-06-08T07:53:22Z","snapshot_observed_at":"2026-08-09T20:01:25.539327Z","submitted_at":"2021-01-05T02:42:58Z","title":"I-BERT: Integer-only BERT Quantization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.01321","snapshot_observed_at":"2026-08-06T21:07:28.507046Z","title":"I-bert: Integer-only bert quantization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.507046Z"},"links":{"cited_paper":"/paper/2101.01321","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:e94a0fa72599ad17df7b052a0cd545347025271170fd93d1e51a2d1aad8b6ec3","observation_id":"499116ea-dfcf-4593-baba-f0ce8171e7c4","resolution":{"observed_at":"2026-08-06T21:07:28.507046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14017","last_updated":"2023-02-27T18:18:13Z","snapshot_observed_at":"2026-08-07T20:46:33.578633Z","submitted_at":"2023-02-27T18:18:13Z","title":"Full Stack Optimization of Transformer Inference: a Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.14017","snapshot_observed_at":"2026-08-06T21:07:28.605678Z","title":"Full stack optimization of transformer inference: a survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.605678Z"},"links":{"cited_paper":"/paper/2302.14017","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:3fc83455f3c026d57e5598983d378d321ffccd6dca1cac70e810a867c95d883f","observation_id":"ff7dada2-ed6b-4b18-94ab-09072f391012","resolution":{"observed_at":"2026-08-06T21:07:28.605678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:36.484635Z","title":"Cambricon-D: Full-network differential acceleration for diffusion models,","venue":null,"work_id":"2f168a6d-5929-4b88-8b3a-a600a64bafd1","year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.636853Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:9916fdc19c53115487e2c166c6105c5443fbefcc56e8d66b2730e1d5e3e2e362","observation_id":"33a1c643-67ef-42e9-8d38-68e818ca0bd4","resolution":{"observed_at":"2026-08-06T21:07:36.585791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:36.345914Z","title":"Packing sparse convolutional neural networks for efficient systolic array implementations: Column combining under joint optimization,","venue":null,"work_id":"bbfd0dae-1ea7-47af-a5c9-57ac010edd93","year":2019},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.662157Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:fd9fc4910d7e1df00accb49eb28113c5f7883f1131dd662aef265b96281c55d5","observation_id":"223eaf43-bad7-40e1-a87a-44a88de2e9eb","resolution":{"observed_at":"2026-08-06T21:07:36.401166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:36.203956Z","title":"Measuring the gap between fpgas and asics,","venue":null,"work_id":"147f9d78-fd94-499d-88d4-175e24aee1e6","year":2006},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.840862Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:97ae0f3d45f1cbdfbc1eb11ca600f42646f42fe5186ce79a0a9a7af0af67550c","observation_id":"f8eaa0ea-590c-4fe0-8f82-9371aae37e7d","resolution":{"observed_at":"2026-08-06T21:07:36.278314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:36.075121Z","title":"Maeri: Enabling flexible dataflow mapping over dnn accelerators via reconfigurable intercon- nects,","venue":null,"work_id":"fc2e265d-95e5-470c-ba21-8a9ac81e0533","year":2018},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:28.973229Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:6c11df5a1236a65308b89b50b2af5bdd979d50c1d9bb4a6f35e1b1b0f0b72afe","observation_id":"dea5ddca-7c5a-4d69-9773-15a20e0fb792","resolution":{"observed_at":"2026-08-06T21:07:36.129614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:29.063165Z","title":"Q-diffusion: Quantizing diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.063165Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:c02e24e7eb02758b3ab25015671c16533c7d29d8d77f2135c0e154b9d9454d9d","observation_id":"7e542467-9c50-49c9-9329-aa75305c2e11","resolution":{"observed_at":"2026-08-06T21:07:29.063165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:35.892659Z","title":"Snapfusion: Text-to-image diffusion model on mobile devices within two seconds,","venue":null,"work_id":"aead2a58-ec7e-44bd-ba63-1bdad15dc12a","year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.144241Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:7c1b3dbf903c810c8f1a318ca3fe2bbd0148eeaa55959625d23c4a6026046ab4","observation_id":"a81acded-d5c9-443d-b2e1-9174b69af8aa","resolution":{"observed_at":"2026-08-06T21:07:35.999945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:35.785909Z","title":"Davinci: A scalable architecture for neural network computing","venue":null,"work_id":"7e908319-6ed9-4208-a6e7-0a1a912c5f33","year":2019},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.234466Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:572f239671486331e852d5c45f0d4d89260387dfab64eb94c023add49cf986c0","observation_id":"4714c64a-a788-4167-ac6e-e14981ccc781","resolution":{"observed_at":"2026-08-06T21:07:35.841590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:29.312104Z","title":"Microsoft coco: Common objects in context,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.312104Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:03af462bd5b03321124780b5ecc973709b677c27e2da7438ba8b759480d26a42","observation_id":"4aa4b7c3-0da9-4f0b-b1da-296e9a2f5481","resolution":{"observed_at":"2026-08-06T21:07:29.312104Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.09778","last_updated":"2022-10-31T09:05:25Z","snapshot_observed_at":"2026-07-06T12:39:36.604492Z","submitted_at":"2022-02-20T10:37:52Z","title":"Pseudo Numerical Methods for Diffusion Models on Manifolds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.09778","snapshot_observed_at":"2026-08-06T21:07:29.385420Z","title":"Pseudo numerical methods for diffusion models on manifolds,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.385420Z"},"links":{"cited_paper":"/paper/2202.09778","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:9f068726a61fc494cdd1e9105cdd8594d1524e38d76f2acb99b9a6bf82a467a3","observation_id":"a6c41ac0-22ab-405b-a95d-7b7c96c85134","resolution":{"observed_at":"2026-08-06T21:07:29.385420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:29.489300Z","title":"Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.489300Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:735e92351c716612a8a1e15ee3699fc0e12061ffb91c591b40900e19a360eb43","observation_id":"a978ccec-0cc4-45e3-8283-83313812b5a7","resolution":{"observed_at":"2026-08-06T21:07:29.489300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:35.600939Z","title":"Sanger: A co-design framework for enabling sparse attention using reconfigurable architecture,","venue":null,"work_id":"fe2f91b5-a536-4b1c-a80c-703bca401f65","year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.581150Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:23a63fbf18a2ec0dc63d49e094dbd3d2b43a9af752299c824a85489db654c7e7","observation_id":"1fac6c3a-3813-42a4-bf08-f4b70697eedc","resolution":{"observed_at":"2026-08-06T21:07:35.687705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:35.461187Z","title":"Flexflow: A flexible dataflow accelerator architecture for convolutional neural networks,","venue":null,"work_id":"689e41ee-d294-48f1-a224-ee1ba8e8c5f7","year":2017},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.660160Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:0af742dc5d0aaf05a36fd0f491d1f995508dcd566ff222d980c61362f381d8c9","observation_id":"94ea35e7-bf54-49ff-87cb-f0f4a01b2ba4","resolution":{"observed_at":"2026-08-06T21:07:35.530460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12524","last_updated":"2022-05-30T03:13:08Z","snapshot_observed_at":"2026-07-06T13:13:40.650733Z","submitted_at":"2022-05-25T06:40:09Z","title":"Accelerating Diffusion Models via Early Stop of the Diffusion Process","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12524","snapshot_observed_at":"2026-08-06T21:07:29.718720Z","title":"Accelerating diffu- sion models via early stop of the diffusion process,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.718720Z"},"links":{"cited_paper":"/paper/2205.12524","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:51ad41fce611a7e399183893f40c2ee55ec5e88b918d8f39d62bdb1f7bd69e67","observation_id":"4d9934ec-53dc-43af-878c-59ad853df272","resolution":{"observed_at":"2026-08-06T21:07:29.718720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:35.329448Z","title":"Deepcache: Accelerating diffusion models for free,","venue":null,"work_id":"0e170f70-928c-45be-976b-9e3d84d2ce73","year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.800109Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:282f78130e4230285027c9e48e6d59d031608680419674103bca78ead9c94a97","observation_id":"5c66eeea-ab27-444d-86de-7a776742bd3a","resolution":{"observed_at":"2026-08-06T21:07:35.402790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:35.221050Z","title":"On distillation of guided diffusion models,","venue":null,"work_id":"320a42fb-f87d-4fc5-a50b-117ea46abee4","year":2023},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.881609Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:ec2b713e88e7482793f2bed01d4d4300b0f0e38822bebd115134ce89f53f84d3","observation_id":"1a7636bd-7e50-4d83-9a76-bef230363d98","resolution":{"observed_at":"2026-08-06T21:07:35.275158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.02867","last_updated":"2018-07-28T06:51:27Z","snapshot_observed_at":"2026-07-06T06:37:55.065033Z","submitted_at":"2018-05-08T07:34:17Z","title":"Online normalizer calculation for softmax","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.02867","snapshot_observed_at":"2026-08-06T21:07:29.988058Z","title":"Online normalizer calculation for softmax,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:29.988058Z"},"links":{"cited_paper":"/paper/1805.02867","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:e29fb53caa550800bccf1b3b100673be925f8c536aed492a3d2b0fe64e7eea14","observation_id":"fa463e38-8513-456a-8886-6d28826c27c3","resolution":{"observed_at":"2026-08-06T21:07:29.988058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.08295","last_updated":"2021-06-15T17:12:42Z","snapshot_observed_at":"2026-08-02T11:19:40.664702Z","submitted_at":"2021-06-15T17:12:42Z","title":"A White Paper on Neural Network Quantization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.08295","snapshot_observed_at":"2026-08-06T21:07:30.127726Z","title":"A white paper on neural network quantization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:30.127726Z"},"links":{"cited_paper":"/paper/2106.08295","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:56d5cf013781ab784f042caa0546f41e848ab205b12740bd413223f8e792d43b","observation_id":"14035a45-2b54-45f3-b44c-6f47dc5c56da","resolution":{"observed_at":"2026-08-06T21:07:30.127726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:35.100427Z","title":"Vitality: Pro- moting serendipitous discovery of academic literature with transformers & visual analytics,","venue":null,"work_id":"38a68c73-d8f7-48ea-b03f-6345c32ee05b","year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:30.292919Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:165c2ad0c2466f485095a182405e4987b2b3c6ca743535f043f125e83afaed54","observation_id":"3f7fe031-1248-41d6-903b-b1c175e7b7af","resolution":{"observed_at":"2026-08-06T21:07:35.159510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:34.955275Z","title":"Nvidia deep learning accelerator","venue":null,"work_id":"ecf0909e-0d1c-4779-abd8-7b68c1afde7e","year":2018},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:30.375455Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:0816f06b0acd47cf0b2c023df0e50376e3beed8679e7f5483f7713925f9fbe3e","observation_id":"5a29d643-91a1-474b-9b63-213bdacc13cb","resolution":{"observed_at":"2026-08-06T21:07:35.027357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14167","last_updated":"2024-02-21T23:08:54Z","snapshot_observed_at":"2026-07-06T17:33:41.634820Z","submitted_at":"2024-02-21T23:08:54Z","title":"T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14167","snapshot_observed_at":"2026-08-06T21:07:30.476425Z","title":"T-stitch: Accelerating sampling in pre-trained diffu- sion models with trajectory stitching,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:30.476425Z"},"links":{"cited_paper":"/paper/2402.14167","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:0babbf932a8053a0634d36455b3efd79b5a1924783013ad82a523ca4c4cfacf3","observation_id":"88e12faf-d1ae-42df-bbb3-78b748c0b107","resolution":{"observed_at":"2026-08-06T21:07:30.476425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:34.799637Z","title":"Hybrid memory cube (hmc),","venue":null,"work_id":"8507f45c-d93a-44a7-9e01-84331482b32c","year":2011},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:30.597026Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:ed6a2bf740bdfdd5b361493b6da465c792d6faacdb088454b24cae7ddb06da14","observation_id":"68f202c4-40ca-44b4-b818-3b63ccde0c72","resolution":{"observed_at":"2026-08-06T21:07:34.848781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:34.695476Z","title":"Sigma: A sparse and irregular gemm ac- celerator with flexible interconnects for dnn training,","venue":null,"work_id":"97b3ce82-1990-4f6e-a0ea-acd6a3cc5f27","year":2020},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:30.769501Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:f0ea6ec1f7a445af9be7bef469c9a0eb29deae8024b42d88006aa3c4ce365259","observation_id":"94605ce7-6766-4cd8-b055-73c23b606894","resolution":{"observed_at":"2026-08-06T21:07:34.737498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:34.566975Z","title":"Dota: detect and omit weak attentions for scalable transformer acceleration,","venue":null,"work_id":"4f7d0fc9-6763-469c-b7ba-8646bd7a0406","year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:30.898468Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:5d3c35cb230dd5a253423a5eb938b33949cc491c6c2b4fb6d23eae1ca38da698","observation_id":"4c55dda5-92f4-4bef-9f45-b3c3518e601a","resolution":{"observed_at":"2026-08-06T21:07:34.619467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:31.055702Z","title":"Zero-shot text-to-image generation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:31.055702Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:a2fa670974c52504126e39d9433f88c064bd3c5e10df14af2d78139a8987699f","observation_id":"007f0cc1-0712-4e2a-9ff6-abf85e4e5afb","resolution":{"observed_at":"2026-08-06T21:07:31.055702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:34.415295Z","title":"High- resolution image synthesis with latent diffusion models,","venue":null,"work_id":"3812c9b9-1f8e-4efa-affc-bc6bce901032","year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:31.218380Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:ddc81d39c8b4771d2fceed275452f78923aedaa7005d0c4e853f64006f6cd820","observation_id":"ce525e2e-9584-4c19-b81a-b65331c3eb55","resolution":{"observed_at":"2026-08-06T21:07:34.498668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12015","last_updated":"2024-03-18T17:51:43Z","snapshot_observed_at":"2026-08-06T10:10:20.333853Z","submitted_at":"2024-03-18T17:51:43Z","title":"Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12015","snapshot_observed_at":"2026-08-06T21:07:31.333439Z","title":"Fast high-resolution image synthesis with latent adversarial diffusion distillation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:31.333439Z"},"links":{"cited_paper":"/paper/2403.12015","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:6a18cdb46d7d877e4fa37f060b70d69f14cb0c0b44f4c9a6a821162da047fb38","observation_id":"48382217-9591-4ada-baec-7863e67baf59","resolution":{"observed_at":"2026-08-06T21:07:31.333439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17042","last_updated":"2023-11-28T18:53:24Z","snapshot_observed_at":"2026-07-06T16:53:58.875152Z","submitted_at":"2023-11-28T18:53:24Z","title":"Adversarial Diffusion Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.17042","snapshot_observed_at":"2026-08-06T21:07:31.491214Z","title":"Adversarial diffusion distillation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:31.491214Z"},"links":{"cited_paper":"/paper/2311.17042","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:151bef981f6c01cb3346fe448bef0cf09660d8dc19c36194b9bcde195fa300cd","observation_id":"25ad2139-25f3-4053-b7c9-9f0f6cd3567b","resolution":{"observed_at":"2026-08-06T21:07:31.491214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:31.578153Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:31.578153Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:2866c7a9b4674f15501e23665a125817f67f15b321c2b2f58b231943a427b546","observation_id":"202cb4e3-a58f-4077-9523-9e0572c30c91","resolution":{"observed_at":"2026-08-06T21:07:31.578153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:34.249432Z","title":"An accelerator for sparse convolutional neural networks leveraging systolic general matrix- matrix multiplication,","venue":null,"work_id":"3e79adb3-7d4b-4cc9-a18c-79231ab213d7","year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:31.664952Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:2999e7ee54e1dc0c6713a8d3c53d78b86437a147ec48b1a7766bbb2b39b44102","observation_id":"84cd4e99-e368-4046-82e8-6bc3f70e9af3","resolution":{"observed_at":"2026-08-06T21:07:34.310387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:31.797563Z","title":"Drq: dynamic region-based quantization for deep neural network acceleration,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:31.797563Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:5581c3eccf9f1355703ccbbd551ce8404a5333e7384c7b04afb12c9be108e517","observation_id":"007f6832-5df7-408c-a030-53696da5fd73","resolution":{"observed_at":"2026-08-06T21:07:31.797563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:34.121553Z","title":"Softermax: Hardware/software co-design of an efficient softmax for transformers,","venue":null,"work_id":"95e4105a-cf69-4e2b-9132-191e03f1bbec","year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:31.853518Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:521140c73c8a1aa668a84e1c7e3618b5b31abc159c6558987fd92026a5c59d81","observation_id":"d5029307-bc6a-4b06-a369-e3aa81b27a4f","resolution":{"observed_at":"2026-08-06T21:07:34.164518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:33.956859Z","title":"Sze, Y .-H","venue":null,"work_id":"3a89bc92-a2d6-4b16-bee7-d199e6425ad9","year":2020},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:31.888955Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:d0a1660ce93450a947c34f88be7ebfaefe77fb0ce14b68589f3aab600ad3d995","observation_id":"29210a47-12c8-49fd-a0f1-c6909e4ff087","resolution":{"observed_at":"2026-08-06T21:07:34.042466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13170","last_updated":"2024-05-21T19:37:00Z","snapshot_observed_at":"2026-07-06T18:17:34.028834Z","submitted_at":"2024-05-21T19:37:00Z","title":"FEATHER: A Reconfigurable Accelerator with Data Reordering Support for Low-Cost On-Chip Dataflow Switching","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13170","snapshot_observed_at":"2026-08-06T21:07:32.037527Z","title":"Feather: A reconfigurable accelerator with data reordering support for low-cost on-chip dataflow switching,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.037527Z"},"links":{"cited_paper":"/paper/2405.13170","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:f6d5a8b4da0482c5321d11a23505fe16ea7927996bbb60f3308a0b0dbb456f76","observation_id":"124132b2-5f15-4b3c-aad4-a8712aca168d","resolution":{"observed_at":"2026-08-06T21:07:32.037527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:33.842731Z","title":"Spatten: Efficient sparse attention architecture with cascade token and head pruning,","venue":null,"work_id":"2c9d7d4e-f489-4df6-980f-84fdc84c0a43","year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.096216Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:6be28c1904d3d851167719169e4a4f53790f298c908d235083c89fc1c84e608e","observation_id":"37724ca2-3429-464a-9245-50263bc72c16","resolution":{"observed_at":"2026-08-06T21:07:33.893259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:33.673950Z","title":"Automated systolic array architecture synthesis for high throughput cnn inference on fpgas,","venue":null,"work_id":"f78ec368-e865-4402-86d7-593ea2e1d003","year":2017},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.230082Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:d7e7bd617a8cc1b64471be3c086799b06dd0f29de29c0b2c3e2eba7b6a012d9d","observation_id":"30e66839-934a-4621-879b-ddcc5dc33f1e","resolution":{"observed_at":"2026-08-06T21:07:33.748789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:33.549934Z","title":"Cache me if you can: Accelerating diffusion models through block caching,","venue":null,"work_id":"fb995e2f-1af1-4d87-a9f1-9ad03bc11f4d","year":2024},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.301976Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:0da5d2b7a3720cf0e7bb2598196b51b4c91709ba22598e12ad33a0bbb176c7e3","observation_id":"6fad87fc-2c3f-498c-bc2d-7f8073fa05d0","resolution":{"observed_at":"2026-08-06T21:07:33.623912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03337","last_updated":"2024-05-24T09:17:29Z","snapshot_observed_at":"2026-08-09T03:27:00.139869Z","submitted_at":"2023-10-05T06:44:13Z","title":"Denoising Diffusion Step-aware Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03337","snapshot_observed_at":"2026-08-06T21:07:32.387584Z","title":"Denoising diffusion step-aware models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.387584Z"},"links":{"cited_paper":"/paper/2310.03337","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:89f7978e8249a57f1812afe2647881b9b261da6e99f884730a9ac7d285e6e310","observation_id":"9f42b9cb-0cde-47d1-a3ff-7cd85d731531","resolution":{"observed_at":"2026-08-06T21:07:32.387584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:33.458211Z","title":"Vitcod: Vision transformer acceleration via dedicated algorithm and accelerator co-design,","venue":null,"work_id":"8ce44571-6672-434b-b78f-0d7c2eef51fd","year":2023},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.493169Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:4b2961f52a7b0decf9990b8a48dcdb60ee95fff52869e4a8bd7ecccd57a84405","observation_id":"a56da747-701c-48ba-b025-87eec6584fcd","resolution":{"observed_at":"2026-08-06T21:07:33.487454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.10789","last_updated":"2022-06-22T01:11:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-06-22T01:11:29Z","title":"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.10789","snapshot_observed_at":"2026-08-06T21:07:32.534698Z","title":"Scaling autoregres- sive models for content-rich text-to-image generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.534698Z"},"links":{"cited_paper":"/paper/2206.10789","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:29729207e815c47ba1b737a29e6aa93b0a9e4b3b8a78ceb28025e88e62a5151c","observation_id":"b7568541-b979-4092-a208-5c609a598400","resolution":{"observed_at":"2026-08-06T21:07:32.534698Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.02191","last_updated":"2021-12-03T23:06:57Z","snapshot_observed_at":"2026-07-06T12:15:13.803759Z","submitted_at":"2021-12-03T23:06:57Z","title":"NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference","version":1},"cited_work":{"arxiv_id":"2112.02191","doi":null,"metadata_source":"pith","pith_arxiv_id":"2112.02191","snapshot_observed_at":"2026-08-06T21:07:32.843788Z","title":"NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference","venue":"cs.LG","work_id":"146992b3-5239-4cf7-b0a6-9ad5d032fc93","year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.614534Z"},"links":{"cited_paper":"/paper/2112.02191","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:a29d54d9cfbe060ef75869d2cddc5a946b4a900769d4a320a178063cb38a6136","observation_id":"342e2629-7c39-4adb-9b1d-5ce67789ce0b","resolution":{"observed_at":"2026-08-06T21:07:32.945823Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:33.312944Z","title":"Optimizing fpga-based accelerator design for deep convolutional neural networks,","venue":null,"work_id":"89369a44-cf5d-454a-85b0-ea1add931262","year":2015},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.728335Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:11ad3cd7d3095dde728d10c0aa1f786786b270f268ffa278f1f6ea1ff46a36bb","observation_id":"0437acc0-d05d-4b9d-accd-60086cecb6c0","resolution":{"observed_at":"2026-08-06T21:07:33.378489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:07:33.221238Z","title":"Zhu, https://github.com/Lyken17/pytorch-OpCounter, 2018","venue":null,"work_id":"f0038f78-d70c-4479-aed1-6f448383d21e","year":2018},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.778533Z"},"links":{"citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:1f32d931ad2dd9a07489ef02f7624d1256f2f2ff5e25a47735aa7ab88c8ac542","observation_id":"7f62dca4-47d0-477f-acf3-e527a5a0d1af","resolution":{"observed_at":"2026-08-06T21:07:33.287471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","latest_version":1,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-06T20:52:38.170454Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations"},"reference_resolution":{"displayed":66,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":28,"verified_exact":1,"verified_fuzzy":37},"total_outbound_references":66},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2507.01309."}