{"as_of":"2026-08-15T14:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fe0c4fc294c6b08abfc9ded8590badd50685a2671732236e8f202e57aa49480a","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:48:01.725050Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-08T04:23:26.079298Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T21:46:42.507564Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"cited_work":{"arxiv_id":"2506.07046","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07046","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforce- ment Learning Compute Engine","venue":null,"work_id":"ce12d1f4-c080-4e9f-94e5-958fe1a95257","year":2025},"citing_paper":{"arxiv_id":"2604.24273","last_updated":"2026-04-27T10:03:37Z","snapshot_observed_at":"2026-07-06T23:10:20.676482Z","submitted_at":"2026-04-27T10:03:37Z","title":"BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-08T04:23:26.079298Z"},"links":{"cited_paper":"/paper/2506.07046","citing_paper":"/paper/2604.24273"},"observation_digest":"sha256:f12325eac0113604a7d1136610dda69785a84883aeae49e7ead95521fccefc8d","observation_id":"2f21c3fe-b503-4270-a5ad-79493d9909d5","resolution":{"observed_at":"2026-05-11T21:46:42.510574Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.07046/citation-record","integrity":"/paper/2506.07046/integrity","json":"/paper/2506.07046/citation-record.json","paper":"/paper/2506.07046"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:48:02.148526Z","title":"QuaRL: Quantization for fast and environmentally sustainable reinforcement learning,","venue":null,"work_id":"d86b07a4-41fc-4ace-8e07-96cbf723e980","year":2022},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.585641Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:2e6f729c8571b34cf1c9bcf7dabe86e819439c655a60b58bb662110f38932560","observation_id":"37108261-7ae3-47d7-8e60-56700315170c","resolution":{"observed_at":"2026-08-07T05:48:02.152697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:02.136000Z","title":"E2HRL: An energy-efficient hardware ac- celerator for hierarchical deep reinforcement learning,","venue":null,"work_id":"ce99b79b-71fc-4e1b-8655-7e2a25512dcf","year":2022},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.590780Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:7e805c7e580b12c9e3888b6e5da7c32b73e7a80abe467d5bea2c1ec303d75cc1","observation_id":"6507f286-bef8-4a96-8524-34fa28d3702b","resolution":{"observed_at":"2026-08-07T05:48:02.140242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:02.123968Z","title":"ChipNEMO: Domain-adapted LLMs for chip design,","venue":null,"work_id":"a05ef171-180d-4086-8e4c-74760360771f","year":2023},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.594879Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:da9376eed8de094e644df706b7cf2c6f9a9bea25b7c578faac713b74c9bc8a3b","observation_id":"bd7e719e-6c98-4e6c-8409-0e239e04e3a4","resolution":{"observed_at":"2026-08-07T05:48:02.127903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.10746","last_updated":"2020-04-22T17:56:07Z","snapshot_observed_at":"2026-08-10T07:39:19.641872Z","submitted_at":"2020-04-22T17:56:07Z","title":"Chip Placement with Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.10746","snapshot_observed_at":"2026-08-07T05:48:01.599158Z","title":"Chip placement with deep reinforce- ment learning,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.599158Z"},"links":{"cited_paper":"/paper/2004.10746","citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:66f58722815bd64c3f09f244bef5102e3d753470f41467926fa7b828629606e1","observation_id":"7835f54b-0261-4e32-a03e-6ee047150aba","resolution":{"observed_at":"2026-08-07T05:48:01.599158Z","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":"2026-08-07T05:48:02.111304Z","title":"Mastering the game of go without human knowledge,","venue":null,"work_id":"3af23452-90e2-4f6a-a5e1-08f7ec1f4671","year":2017},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.603869Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:7da8fedce57d2b6787ec13ff7cbbec650c369c960ceb1f60fbdcab6fe40b6b2a","observation_id":"ade0bb06-5214-492f-8adb-69e95d51c0f6","resolution":{"observed_at":"2026-08-07T05:48:02.115798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:02.098928Z","title":"Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods,","venue":null,"work_id":"02aa7228-c280-4aca-a74c-2a47980553d7","year":2024},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.607836Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:8395889eb9f2cc188d6548e2233063de209aa3571ccdb4e13cf9e573729b88ac","observation_id":"4b7e2ea6-cdcf-4788-bd61-ad5e83767aa5","resolution":{"observed_at":"2026-08-07T05:48:02.103085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:02.084906Z","title":"A 2.1TFLOPS/W Mobile Deep RL Accelerator with Transposable PE Array and Experience Compression,","venue":null,"work_id":"3b35ed25-fd69-448a-93f3-48f3076b1c55","year":2019},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.612559Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:5b85d2598b76c5e6f8edd9f1ce934f2ba973ddbfd0cec9e057635a7f517f1772","observation_id":"71df8ebb-fd88-408c-bf3d-1a7c798fabd1","resolution":{"observed_at":"2026-08-07T05:48:02.090023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:02.071285Z","title":"Explainable Reinforce- ment Learning: A Survey and Comparative Review,","venue":null,"work_id":"57664677-7fac-46c3-bd64-33a3a417bd7f","year":2024},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.616477Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:573f706679c66d75c502644a3f14b0a708f22afd9d35e3ca5a271653e4fdcb2c","observation_id":"74ee6d44-6504-4a87-ba93-e8155e7c48c1","resolution":{"observed_at":"2026-08-07T05:48:02.075847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:02.056750Z","title":"Efficient and scalable reinforcement learning for large-scale network control,","venue":null,"work_id":"51e9287d-cd88-4e61-9627-ccbcb6f12357","year":2024},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.620490Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:2f3f8a903141f69f0092b87a1e34207638cb7773b8bc1849b3a0151ed1fdd02e","observation_id":"246b4aad-45eb-4bf0-aea5-a3f0009997b4","resolution":{"observed_at":"2026-08-07T05:48:02.061978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T05:48:01.624340Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.624340Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:e95b5098905cc3ada9217d9c5384fa8076926287d331bd4a755850b66bce5428","observation_id":"b1193ce1-c813-4fd1-a759-4b2a253d3d8c","resolution":{"observed_at":"2026-08-07T05:48:01.624340Z","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":"2026-08-07T05:48:02.042303Z","title":"Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads,","venue":null,"work_id":"776d70c8-3222-4f24-900c-f66916a516ed","year":2025},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.628488Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:f9d6b0e250e627d7e59f98a10e3787fb8a99fb58d1eb68a7453c29014f6c9a49","observation_id":"2faea4d1-3aa4-427b-b2f6-ca8891fedf59","resolution":{"observed_at":"2026-08-07T05:48:02.047458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:02.029486Z","title":"Flex-SFU: Activation Function Acceleration with Non-Uniform Piecewise Approximation,","venue":null,"work_id":"a0a1a672-9e65-43eb-9662-fef9ed345543","year":2025},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.632557Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:54b062f289c4fbac71124b13b76d649bc93b703e710d081087cf8c7f059c5366","observation_id":"44e07882-eb7f-4908-91ce-49206a4f1166","resolution":{"observed_at":"2026-08-07T05:48:02.033649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:02.016912Z","title":"LPRE: Logarithmic Posit-enabled Reconfigurable edge-AI Engine,","venue":null,"work_id":"e6988fc5-2beb-4be1-a5bc-40baeb77ed39","year":2025},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.636494Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:356366694d1adcfc3feb92848d7eeace474e51ec66c044c3a03da44b7e66de95","observation_id":"fd6b7e1a-a967-49f2-9ef1-30f7327895b0","resolution":{"observed_at":"2026-08-07T05:48:02.021147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.640492Z","title":"A Reconfigurable Processing Element for Multiple- Precision Floating/Fixed-Point HPC,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.640492Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:781cb02f7f34be033f02cba17579104624f919ffa8a5a3e0a8b2e844cb1c9ed3","observation_id":"a99cbea8-66e5-4776-95b2-93fca4904462","resolution":{"observed_at":"2026-08-07T05:48:01.640492Z","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":"2026-08-07T05:48:01.644269Z","title":"A Configurable Floating-Point Multiple-Precision Processing Element for HPC and AI Converged Computing,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.644269Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:c633ef1e10fe921252076337ac00b83bac0f4a4d8c6c2c3d91b3137b436f382d","observation_id":"81eea055-2b19-4d60-a0b4-322a72cccb61","resolution":{"observed_at":"2026-08-07T05:48:01.644269Z","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":"2026-08-07T05:48:01.989090Z","title":"High-Performance Accurate and Approximate Multipliers for FPGA-Based Hardware Ac- celerators,","venue":null,"work_id":"31bf9390-229c-4168-bf8f-440cd58d0488","year":2022},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.648043Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:e234351f377752e9ae462e91b56c308b2d714e10b8ae25d39a07912f7e593f9c","observation_id":"a9132cb6-74b6-41ea-b578-fd544a109c1d","resolution":{"observed_at":"2026-08-07T05:48:01.993602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.976602Z","title":"QuantMAC: Enhancing Hardware Perfor- mance in DNNs With Quantize Enabled Multiply-Accumulate Unit,","venue":null,"work_id":"1e115be2-2a76-49a8-8c76-440c8a57ba57","year":2024},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.652266Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:47d88bb7cff90ba8f33a5aad12a3a619047f40a59cb1224719ba35c127ce350a","observation_id":"a5c06eba-12c6-4c55-8dc5-c2babc8453f6","resolution":{"observed_at":"2026-08-07T05:48:01.980699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.656072Z","title":"Unified Posit/IEEE-754 Vector MAC Unit for Transprecision Computing,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.656072Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:43789923df3df9642c46dd7911bf7c38ebb65236a98e6766ff1255617e2b9090","observation_id":"629185e5-4992-406d-b853-4cfb0b349e7b","resolution":{"observed_at":"2026-08-07T05:48:01.656072Z","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":"2026-08-07T05:48:01.662167Z","title":"A Low-Cost Floating-Point FMA Unit Supporting Package Operations for HPC-AI Applications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.662167Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:e2059ae43c659a20dda89de28d5dea1a3efc0349dfe73aa16b222eae0e759792","observation_id":"3d256177-3af0-4663-9079-7effd5cf8dea","resolution":{"observed_at":"2026-08-07T05:48:01.662167Z","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":"2026-08-07T05:48:01.665961Z","title":"A Low-Cost Floating-Point Dot-Product-Dual- Accumulate Architecture for HPC-Enabled AI,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.665961Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:5e4f6863bec90deeb83852332176f61f5a2d77bb33607fa13a833e332463c951","observation_id":"2551d4ff-1955-4c63-9796-4544340a930f","resolution":{"observed_at":"2026-08-07T05:48:01.665961Z","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":"2026-08-07T05:48:01.941352Z","title":"A Vector Systolic Accelerator for Multi- Precision Floating-Point High-Performance Computing,","venue":null,"work_id":"9e1faed6-5830-4c96-ae79-b65a3f0faa88","year":2022},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.670032Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:cb0d41110c4282b2935d21dde5c36a4c4ccd4b720b3d64fedf60170152bd5154","observation_id":"01f6d90c-3c9c-4d4b-9f83-5674bd3290e3","resolution":{"observed_at":"2026-08-07T05:48:01.945275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.928821Z","title":"Multiple-Mode- Supporting Floating-Point FMA Unit for Deep Learning Processors,","venue":null,"work_id":"b527d8f4-35fe-44bc-8274-5f9ee0c3fdc4","year":2023},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.674118Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:ed7bbd6523a25dfa4502b7c2a6b4f7d9adc30fbc72aaaa26c12c0bccb11fe1c5","observation_id":"aa03afe7-fe5a-4acb-8d7c-a88f170d6e40","resolution":{"observed_at":"2026-08-07T05:48:01.932959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.916857Z","title":"A Two-Stage Operand Trimming Approximate Logarithmic Multiplier,","venue":null,"work_id":"423af3e0-af0d-4f30-9ceb-24eeea79d424","year":2022},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.678380Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:573736b20e5841079ff09440f3a4f1db292185f40472b986375b8ac8e446413e","observation_id":"dbce18f3-3a63-461c-9927-4852f6a49128","resolution":{"observed_at":"2026-08-07T05:48:01.920939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.903710Z","title":"An Empirical Approach to Enhance Performance for Scalable CORDIC-Based Deep Neural Networks,","venue":null,"work_id":"337858c2-51d3-4bcd-bd4a-1e6cc1f1b23a","year":2023},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.682969Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:cec1ec722b12c7c1be7d1500b5641a06826f232829db30c8c193ba512632d6ff","observation_id":"e8f5dafe-6571-4f9f-9bc7-a7a1d1c1fce9","resolution":{"observed_at":"2026-08-07T05:48:01.908257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.891123Z","title":"Efficient CORDIC-Based Activation Functions for RNN Acceleration on FPGAs,","venue":null,"work_id":"902cdeb7-6a76-4e65-b438-bd9f7e21b73e","year":2025},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.686866Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:f34d221004f04ae8f53680c3ae421b4c24a1d163f5510eeb16946e945ace734e","observation_id":"0c8ea805-c4fe-460f-a6b0-0e0ae0528589","resolution":{"observed_at":"2026-08-07T05:48:01.895097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.878078Z","title":"Approximate Softmax Functions for Energy-Efficient Deep Neural Networks,","venue":null,"work_id":"540a701b-a332-45a8-923e-cd42b59aa77f","year":2023},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.691559Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:47ca45c29d04636b0de0dbdeac097db41a52870357c1c0d72ec0b2613c45dd08","observation_id":"148ef7ae-48b0-45dd-ad87-b2fdd5224180","resolution":{"observed_at":"2026-08-07T05:48:01.882984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.865289Z","title":"A Unified Parallel CORDIC- Based Hardware Architecture for LSTM Network Acceleration,","venue":null,"work_id":"2f2a2455-d8db-47ae-b784-0de96dce2378","year":2023},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.696099Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:1317c8a0993a069337421842ae564bb58881d4babb65774b6ff43b1c14b21d9e","observation_id":"c9d87d85-6b04-4116-abc4-f85dd43b90bd","resolution":{"observed_at":"2026-08-07T05:48:01.869718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.851872Z","title":"Synergy: An HW/SW Framework for High Throughput CNNs on Embedded Heterogeneous SoC,","venue":null,"work_id":"75358857-968f-41ca-9b1f-a8d8eaa2f17e","year":2019},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.699788Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:1e548b206337f1d4b855cc15c5f9d60ba0b0e3870b12536a20ef1fc26766e47d","observation_id":"0d737602-ec64-49db-b89b-11a37e75f96b","resolution":{"observed_at":"2026-08-07T05:48:01.856018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.839102Z","title":"Real-Time SSDLite Object Detection on FPGA,","venue":null,"work_id":"456a3448-fe49-42e0-8ac7-fee49a7689af","year":2022},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.704728Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:eb6a6a58701f85567a622becc385732e0193086eb655328dbcc3716b927e1257","observation_id":"87d0b813-fbb3-4266-b086-006b005103e5","resolution":{"observed_at":"2026-08-07T05:48:01.843475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.826436Z","title":"ShortcutFusion: From Tensorflow to FPGA-Based Accelerator With a Reuse-Aware Memory Allocation for Shortcut Data,","venue":null,"work_id":"9d328ef2-aa47-4125-9342-52723b7e03fc","year":2022},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.708561Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:4b7618a03b077c7e62a6d9be21a4e9f57dbb66e9191c0120514b13e9f4aef7c6","observation_id":"a331e366-71b1-42df-85aa-a669bde83257","resolution":{"observed_at":"2026-08-07T05:48:01.830684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.814165Z","title":"A High-Throughput Full-Dataflow Mo- bileNetv2 Accelerator on Edge FPGA,","venue":null,"work_id":"38c291a3-2c7a-4751-8864-640df95800ab","year":2023},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.712363Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:3754f6825f7afc54449f1abe0e902ab7253b5133b0e1bffcf42cb15e62414f2f","observation_id":"36ad0d91-b777-4c2f-b1d4-c0f25e592fea","resolution":{"observed_at":"2026-08-07T05:48:01.818366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.800507Z","title":"A Real-Time Object Detection Processor With xnor-Based Variable-Precision Computing Unit,","venue":null,"work_id":"c8ec1cbf-6091-4f43-ab86-47260121e976","year":2023},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.716206Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:921e185c9940b8d4c7d9403e5abca5363bb162fef11aa0f2789390d7ba7f9360","observation_id":"ce62761c-4321-4d96-b17a-c46a9a4ee973","resolution":{"observed_at":"2026-08-07T05:48:01.804674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:48:01.720546Z","title":"Edge-Side Fine-Grained Sparse CNN Accelerator With Efficient Dynamic Pruning Scheme,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.720546Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:551abd068d377ffa4aa2dea6e73936388af2ae576aa16a4d3d70f641ffee0492","observation_id":"014935f4-98b4-4e1f-ae5f-ffbec7b70a0d","resolution":{"observed_at":"2026-08-07T05:48:01.720546Z","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":"2026-08-07T05:48:01.777984Z","title":"Low Latency Hybrid CORDIC Algorithm,","venue":null,"work_id":"ac01a53b-caad-458f-818b-4ec17cfdddcc","year":2014},"citing_paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:01.725050Z"},"links":{"citing_paper":"/paper/2506.07046"},"observation_digest":"sha256:c05f974163416682c7ec8a8cf817b2dcd63cba0a8a11e113eb250993f60ef218","observation_id":"c9c13608-a3b8-40f8-990b-3c48b9f02628","resolution":{"observed_at":"2026-08-07T05:48:01.784249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.07046","last_updated":"2025-06-08T08:55:49Z","latest_version":1,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-15T13:35:26.110779Z","submitted_at":"2025-06-08T08:55:49Z","title":"QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":26},"total_outbound_references":34},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2506.07046."}