{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:DN2SXNUAMMTL6DX7M3Y5RI3AFV","short_pith_number":"pith:DN2SXNUA","canonical_record":{"source":{"id":"1511.05552","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2015-11-17T02:20:37Z","cross_cats_sorted":[],"title_canon_sha256":"4b4a09ace231affde0c0c8590c9e35749bc01bffe177f26a627cb92d074a930a","abstract_canon_sha256":"301482a3c460bece0c8e563f21d634ada714aad33aeaba5a01722997383fbe7d"},"schema_version":"1.0"},"canonical_sha256":"1b752bb6806326bf0eff66f1d8a3602d6b76b36c822fecda272d0f7d70e164cb","source":{"kind":"arxiv","id":"1511.05552","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.05552","created_at":"2026-05-18T01:19:38Z"},{"alias_kind":"arxiv_version","alias_value":"1511.05552v4","created_at":"2026-05-18T01:19:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.05552","created_at":"2026-05-18T01:19:38Z"},{"alias_kind":"pith_short_12","alias_value":"DN2SXNUAMMTL","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"DN2SXNUAMMTL6DX7","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"DN2SXNUA","created_at":"2026-05-18T12:29:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:DN2SXNUAMMTL6DX7M3Y5RI3AFV","target":"record","payload":{"canonical_record":{"source":{"id":"1511.05552","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2015-11-17T02:20:37Z","cross_cats_sorted":[],"title_canon_sha256":"4b4a09ace231affde0c0c8590c9e35749bc01bffe177f26a627cb92d074a930a","abstract_canon_sha256":"301482a3c460bece0c8e563f21d634ada714aad33aeaba5a01722997383fbe7d"},"schema_version":"1.0"},"canonical_sha256":"1b752bb6806326bf0eff66f1d8a3602d6b76b36c822fecda272d0f7d70e164cb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:38.360094Z","signature_b64":"UVPdShRSmS3S2Aeeu6toQf6sGpKD7q6FCr54l0aYy71fMcTs+yfnP756O/Wgq4fqpuKPl8FucV0FU2J44f3eDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b752bb6806326bf0eff66f1d8a3602d6b76b36c822fecda272d0f7d70e164cb","last_reissued_at":"2026-05-18T01:19:38.359579Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:38.359579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.05552","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:19:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lNPjVY+M9tGUJP0yDGPK+gjuafziGS6b81aasS/xUPzWtM17fliwzHuLVeTVevuyJmu2vPDK8yQ36TUv0fbECw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T02:03:28.877393Z"},"content_sha256":"ddcf3347c5de4c7ebf76f09d89c6398b5e527f59a98a0a3db353e19017470f3a","schema_version":"1.0","event_id":"sha256:ddcf3347c5de4c7ebf76f09d89c6398b5e527f59a98a0a3db353e19017470f3a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:DN2SXNUAMMTL6DX7M3Y5RI3AFV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recurrent Neural Networks Hardware Implementation on FPGA","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Andre Xian Ming Chang, Berin Martini, Eugenio Culurciello","submitted_at":"2015-11-17T02:20:37Z","abstract_excerpt":"Recurrent Neural Networks (RNNs) have the ability to retain memory and learn data sequences. Due to the recurrent nature of RNNs, it is sometimes hard to parallelize all its computations on conventional hardware. CPUs do not currently offer large parallelism, while GPUs offer limited parallelism due to sequential components of RNN models. In this paper we present a hardware implementation of Long-Short Term Memory (LSTM) recurrent network on the programmable logic Zynq 7020 FPGA from Xilinx. We implemented a RNN with $2$ layers and $128$ hidden units in hardware and it has been tested using a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.05552","kind":"arxiv","version":4},"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":"2026-05-18T01:19:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mLnGB4IZr9SIFKwjJoRlWT4XidUWm8DJrGVwdblTlocK6UeXg+rclwPnMmLzb/akqa/O0xhdHmQFTsRxpkLTDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T02:03:28.877771Z"},"content_sha256":"55bef0d6dbbcc65fee10793b285cc5f7b09330ec5965855b639fe0dbf7cc296b","schema_version":"1.0","event_id":"sha256:55bef0d6dbbcc65fee10793b285cc5f7b09330ec5965855b639fe0dbf7cc296b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DN2SXNUAMMTL6DX7M3Y5RI3AFV/bundle.json","state_url":"https://pith.science/pith/DN2SXNUAMMTL6DX7M3Y5RI3AFV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DN2SXNUAMMTL6DX7M3Y5RI3AFV/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":"2026-06-02T02:03:28Z","links":{"resolver":"https://pith.science/pith/DN2SXNUAMMTL6DX7M3Y5RI3AFV","bundle":"https://pith.science/pith/DN2SXNUAMMTL6DX7M3Y5RI3AFV/bundle.json","state":"https://pith.science/pith/DN2SXNUAMMTL6DX7M3Y5RI3AFV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DN2SXNUAMMTL6DX7M3Y5RI3AFV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:DN2SXNUAMMTL6DX7M3Y5RI3AFV","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":"301482a3c460bece0c8e563f21d634ada714aad33aeaba5a01722997383fbe7d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2015-11-17T02:20:37Z","title_canon_sha256":"4b4a09ace231affde0c0c8590c9e35749bc01bffe177f26a627cb92d074a930a"},"schema_version":"1.0","source":{"id":"1511.05552","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.05552","created_at":"2026-05-18T01:19:38Z"},{"alias_kind":"arxiv_version","alias_value":"1511.05552v4","created_at":"2026-05-18T01:19:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.05552","created_at":"2026-05-18T01:19:38Z"},{"alias_kind":"pith_short_12","alias_value":"DN2SXNUAMMTL","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"DN2SXNUAMMTL6DX7","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"DN2SXNUA","created_at":"2026-05-18T12:29:17Z"}],"graph_snapshots":[{"event_id":"sha256:55bef0d6dbbcc65fee10793b285cc5f7b09330ec5965855b639fe0dbf7cc296b","target":"graph","created_at":"2026-05-18T01:19:38Z","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":"Recurrent Neural Networks (RNNs) have the ability to retain memory and learn data sequences. Due to the recurrent nature of RNNs, it is sometimes hard to parallelize all its computations on conventional hardware. CPUs do not currently offer large parallelism, while GPUs offer limited parallelism due to sequential components of RNN models. In this paper we present a hardware implementation of Long-Short Term Memory (LSTM) recurrent network on the programmable logic Zynq 7020 FPGA from Xilinx. We implemented a RNN with $2$ layers and $128$ hidden units in hardware and it has been tested using a ","authors_text":"Andre Xian Ming Chang, Berin Martini, Eugenio Culurciello","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2015-11-17T02:20:37Z","title":"Recurrent Neural Networks Hardware Implementation on FPGA"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.05552","kind":"arxiv","version":4},"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:ddcf3347c5de4c7ebf76f09d89c6398b5e527f59a98a0a3db353e19017470f3a","target":"record","created_at":"2026-05-18T01:19:38Z","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":"301482a3c460bece0c8e563f21d634ada714aad33aeaba5a01722997383fbe7d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2015-11-17T02:20:37Z","title_canon_sha256":"4b4a09ace231affde0c0c8590c9e35749bc01bffe177f26a627cb92d074a930a"},"schema_version":"1.0","source":{"id":"1511.05552","kind":"arxiv","version":4}},"canonical_sha256":"1b752bb6806326bf0eff66f1d8a3602d6b76b36c822fecda272d0f7d70e164cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1b752bb6806326bf0eff66f1d8a3602d6b76b36c822fecda272d0f7d70e164cb","first_computed_at":"2026-05-18T01:19:38.359579Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:19:38.359579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UVPdShRSmS3S2Aeeu6toQf6sGpKD7q6FCr54l0aYy71fMcTs+yfnP756O/Wgq4fqpuKPl8FucV0FU2J44f3eDw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:19:38.360094Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.05552","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ddcf3347c5de4c7ebf76f09d89c6398b5e527f59a98a0a3db353e19017470f3a","sha256:55bef0d6dbbcc65fee10793b285cc5f7b09330ec5965855b639fe0dbf7cc296b"],"state_sha256":"6564906d9bf720d897782fa31fc92b218370522ab545890ce0e43bca4efff9f9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uNyQFgY95vJQXrlS0spIq/OrEkU+EJR1Ori1eytoOkfecAA+lyywdSH/ArJ97C9NrZq3hhyGNRTlV74xdSpUBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T02:03:28.879784Z","bundle_sha256":"7d3cefd8708d8d68644dfb3207c45dac1e8d5b5009604b290a75f7b8db8f08d9"}}