{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ZOSXP3ZSP2JCWPQ6SA3RZTWSGC","short_pith_number":"pith:ZOSXP3ZS","canonical_record":{"source":{"id":"1803.10548","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-28T11:56:20Z","cross_cats_sorted":[],"title_canon_sha256":"adc114d3c3e9970ae4e72e7d7816b70bc10d73e2819a593e6a06e6ba83d3aa97","abstract_canon_sha256":"736cf38c3ae4b827c8d88b5fc5867b4fa0b91b9a48a424246fdd90b2518c44cd"},"schema_version":"1.0"},"canonical_sha256":"cba577ef327e922b3e1e90371cced2308ede1e3ebf6f8822ebd71511304b6183","source":{"kind":"arxiv","id":"1803.10548","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.10548","created_at":"2026-05-18T00:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"1803.10548v3","created_at":"2026-05-18T00:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10548","created_at":"2026-05-18T00:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"ZOSXP3ZSP2JC","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZOSXP3ZSP2JCWPQ6","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZOSXP3ZS","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ZOSXP3ZSP2JCWPQ6SA3RZTWSGC","target":"record","payload":{"canonical_record":{"source":{"id":"1803.10548","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-28T11:56:20Z","cross_cats_sorted":[],"title_canon_sha256":"adc114d3c3e9970ae4e72e7d7816b70bc10d73e2819a593e6a06e6ba83d3aa97","abstract_canon_sha256":"736cf38c3ae4b827c8d88b5fc5867b4fa0b91b9a48a424246fdd90b2518c44cd"},"schema_version":"1.0"},"canonical_sha256":"cba577ef327e922b3e1e90371cced2308ede1e3ebf6f8822ebd71511304b6183","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:40.788607Z","signature_b64":"JQUVrT9d8bI43u8y3UKLKUh1OZgfNTIKm4Uz1o1pkAaeX2J8Sf775cUaevc8LQIoI0SbR28Ic17J2yUddmIYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cba577ef327e922b3e1e90371cced2308ede1e3ebf6f8822ebd71511304b6183","last_reissued_at":"2026-05-18T00:18:40.788091Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:40.788091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.10548","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pHcJ2o3yNjOiOnesNBDlmmTLfBkrvwF5DecgXBriq0BbjUzfkKePTK/9IcNm6An5MwDF41onqasYDHvPK4odAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T05:05:12.538799Z"},"content_sha256":"17d68547c35deb7b2141db5a558a77f5890776d02730af22b6134d4de043a1cd","schema_version":"1.0","event_id":"sha256:17d68547c35deb7b2141db5a558a77f5890776d02730af22b6134d4de043a1cd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ZOSXP3ZSP2JCWPQ6SA3RZTWSGC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FPGA Implementations of 3D-SIMD Processor Architecture for Deep Neural Networks Using Relative Indexed Compressed Sparse Filter Encoding Format and Stacked Filters Stationary Flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nianhong Liu, Sheng Zhang, Yuechao Gao","submitted_at":"2018-03-28T11:56:20Z","abstract_excerpt":"It is a challenging task to deploy computationally and memory intensive State-of-the-art deep neural networks (DNNs) on embedded systems with limited hardware resources and power budgets. Recently developed techniques like Deep Compression make it possible to fit large DNNs, such as AlexNet and VGGNet, fully in on-chip SRAM. But sparse networks compressed using existing encoding formats, like CSR or CSC, complex the computation at runtime due to their irregular memory access characteristics. In [1], we introduce a computation dataflow, stacked filters stationary dataflow (SFS), and a correspon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10548","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E5Wa9YW+HjrfBk/MfLX5rWjO8GB8lahLJ7wiOxZFVZlTqxa9SWpgwbeE45bNx560+htspPIDJm+GZ89Fjkv7AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T05:05:12.539144Z"},"content_sha256":"4aaf0836edbd7be4b79c0e874da1a37be4c86e88b1b2c71a72af0f3a6537ab5d","schema_version":"1.0","event_id":"sha256:4aaf0836edbd7be4b79c0e874da1a37be4c86e88b1b2c71a72af0f3a6537ab5d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZOSXP3ZSP2JCWPQ6SA3RZTWSGC/bundle.json","state_url":"https://pith.science/pith/ZOSXP3ZSP2JCWPQ6SA3RZTWSGC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZOSXP3ZSP2JCWPQ6SA3RZTWSGC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T05:05:12Z","links":{"resolver":"https://pith.science/pith/ZOSXP3ZSP2JCWPQ6SA3RZTWSGC","bundle":"https://pith.science/pith/ZOSXP3ZSP2JCWPQ6SA3RZTWSGC/bundle.json","state":"https://pith.science/pith/ZOSXP3ZSP2JCWPQ6SA3RZTWSGC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZOSXP3ZSP2JCWPQ6SA3RZTWSGC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZOSXP3ZSP2JCWPQ6SA3RZTWSGC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"736cf38c3ae4b827c8d88b5fc5867b4fa0b91b9a48a424246fdd90b2518c44cd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-28T11:56:20Z","title_canon_sha256":"adc114d3c3e9970ae4e72e7d7816b70bc10d73e2819a593e6a06e6ba83d3aa97"},"schema_version":"1.0","source":{"id":"1803.10548","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.10548","created_at":"2026-05-18T00:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"1803.10548v3","created_at":"2026-05-18T00:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10548","created_at":"2026-05-18T00:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"ZOSXP3ZSP2JC","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZOSXP3ZSP2JCWPQ6","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZOSXP3ZS","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:4aaf0836edbd7be4b79c0e874da1a37be4c86e88b1b2c71a72af0f3a6537ab5d","target":"graph","created_at":"2026-05-18T00:18:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"It is a challenging task to deploy computationally and memory intensive State-of-the-art deep neural networks (DNNs) on embedded systems with limited hardware resources and power budgets. Recently developed techniques like Deep Compression make it possible to fit large DNNs, such as AlexNet and VGGNet, fully in on-chip SRAM. But sparse networks compressed using existing encoding formats, like CSR or CSC, complex the computation at runtime due to their irregular memory access characteristics. In [1], we introduce a computation dataflow, stacked filters stationary dataflow (SFS), and a correspon","authors_text":"Nianhong Liu, Sheng Zhang, Yuechao Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-28T11:56:20Z","title":"FPGA Implementations of 3D-SIMD Processor Architecture for Deep Neural Networks Using Relative Indexed Compressed Sparse Filter Encoding Format and Stacked Filters Stationary Flow"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10548","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:17d68547c35deb7b2141db5a558a77f5890776d02730af22b6134d4de043a1cd","target":"record","created_at":"2026-05-18T00:18:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"736cf38c3ae4b827c8d88b5fc5867b4fa0b91b9a48a424246fdd90b2518c44cd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-28T11:56:20Z","title_canon_sha256":"adc114d3c3e9970ae4e72e7d7816b70bc10d73e2819a593e6a06e6ba83d3aa97"},"schema_version":"1.0","source":{"id":"1803.10548","kind":"arxiv","version":3}},"canonical_sha256":"cba577ef327e922b3e1e90371cced2308ede1e3ebf6f8822ebd71511304b6183","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cba577ef327e922b3e1e90371cced2308ede1e3ebf6f8822ebd71511304b6183","first_computed_at":"2026-05-18T00:18:40.788091Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:40.788091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JQUVrT9d8bI43u8y3UKLKUh1OZgfNTIKm4Uz1o1pkAaeX2J8Sf775cUaevc8LQIoI0SbR28Ic17J2yUddmIYCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:40.788607Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.10548","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:17d68547c35deb7b2141db5a558a77f5890776d02730af22b6134d4de043a1cd","sha256:4aaf0836edbd7be4b79c0e874da1a37be4c86e88b1b2c71a72af0f3a6537ab5d"],"state_sha256":"da5854909a2586857bea56d8529592ddb2ab48ecc3faaf136f5fadaba1d65f5d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"82rcHiIQa0JAD21aCAI7EesZYNTs7Cm8qDkTukcfp7JfnUTPOBxQUtuGhofxeDZt232gdpUoGF1t9xdb9AeUCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T05:05:12.541411Z","bundle_sha256":"d967a0c803f523c24a7eb842e4a78d44ba745a59fcee6e341c4cfdde005d65c5"}}