{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:QAQNTSW5DWY4NWYQHXKIOAHDPS","short_pith_number":"pith:QAQNTSW5","canonical_record":{"source":{"id":"2303.12914","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T21:09:49Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"06049c10efd3dad543ce9269ae2a5f0ebd4d1137dc6bca56d2b29610f6a4ada5","abstract_canon_sha256":"f8f0a02b3de2bc9c3d2cf7702914e97c364bfcb7a12c8f9f1adf6578a46bfe40"},"schema_version":"1.0"},"canonical_sha256":"8020d9cadd1db1c6db103dd48700e37cbff0bf1a987791a59286c9b3bc3d7a35","source":{"kind":"arxiv","id":"2303.12914","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.12914","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"arxiv_version","alias_value":"2303.12914v1","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.12914","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"pith_short_12","alias_value":"QAQNTSW5DWY4","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"pith_short_16","alias_value":"QAQNTSW5DWY4NWYQ","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"pith_short_8","alias_value":"QAQNTSW5","created_at":"2026-07-05T05:53:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:QAQNTSW5DWY4NWYQHXKIOAHDPS","target":"record","payload":{"canonical_record":{"source":{"id":"2303.12914","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T21:09:49Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"06049c10efd3dad543ce9269ae2a5f0ebd4d1137dc6bca56d2b29610f6a4ada5","abstract_canon_sha256":"f8f0a02b3de2bc9c3d2cf7702914e97c364bfcb7a12c8f9f1adf6578a46bfe40"},"schema_version":"1.0"},"canonical_sha256":"8020d9cadd1db1c6db103dd48700e37cbff0bf1a987791a59286c9b3bc3d7a35","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:53:55.456275Z","signature_b64":"jEOhBtNkU/PUb58I4k+msVJtC3GU1/hwrvYqHFx7OKAYZd8rBiAXQkEtrHaxjU1dt/je+ZlTQqkFql5cYZvRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8020d9cadd1db1c6db103dd48700e37cbff0bf1a987791a59286c9b3bc3d7a35","last_reissued_at":"2026-07-05T05:53:55.455868Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:53:55.455868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.12914","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:53:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tt3ccyPcahSDH0GwvQfY+MJ9shcfnltL/JO1ocnzjVNlE1lIkPsdQv6k7G6zvs5g905e2M+rZMZYQtakNlE5Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:18:26.314158Z"},"content_sha256":"bbf485e1ecb76b3e67e220656527654b604e78a32242386cd66b7b16e1f3851b","schema_version":"1.0","event_id":"sha256:bbf485e1ecb76b3e67e220656527654b604e78a32242386cd66b7b16e1f3851b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:QAQNTSW5DWY4NWYQHXKIOAHDPS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Febin Sunny, Mahdi Nikdast, Salma Afifi, Sudeep Pasricha","submitted_at":"2023-03-22T21:09:49Z","abstract_excerpt":"Transformer neural networks are rapidly being integrated into state-of-the-art solutions for natural language processing (NLP) and computer vision. However, the complex structure of these models creates challenges for accelerating their execution on conventional electronic platforms. We propose the first silicon photonic hardware neural network accelerator called TRON for transformer-based models such as BERT, and Vision Transformers. Our analysis demonstrates that TRON exhibits at least 14x better throughput and 8x better energy efficiency, in comparison to state-of-the-art transformer accele"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.12914","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.12914/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T05:53:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EnKuaj2cE7uiw8oNz4V/LjkoI3N9E4PwxCmNNd496aAM8z617kyMxvXG/lQ66m1V7BuVE2ocT3DfqACAvCABBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:18:26.314665Z"},"content_sha256":"93bd988259da9eaddfaee5a9bf1da3b081a3686a4dfcfba4d8cbee3963d1fcf1","schema_version":"1.0","event_id":"sha256:93bd988259da9eaddfaee5a9bf1da3b081a3686a4dfcfba4d8cbee3963d1fcf1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QAQNTSW5DWY4NWYQHXKIOAHDPS/bundle.json","state_url":"https://pith.science/pith/QAQNTSW5DWY4NWYQHXKIOAHDPS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QAQNTSW5DWY4NWYQHXKIOAHDPS/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":"2026-08-04T16:18:26Z","links":{"resolver":"https://pith.science/pith/QAQNTSW5DWY4NWYQHXKIOAHDPS","bundle":"https://pith.science/pith/QAQNTSW5DWY4NWYQHXKIOAHDPS/bundle.json","state":"https://pith.science/pith/QAQNTSW5DWY4NWYQHXKIOAHDPS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QAQNTSW5DWY4NWYQHXKIOAHDPS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QAQNTSW5DWY4NWYQHXKIOAHDPS","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":"f8f0a02b3de2bc9c3d2cf7702914e97c364bfcb7a12c8f9f1adf6578a46bfe40","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T21:09:49Z","title_canon_sha256":"06049c10efd3dad543ce9269ae2a5f0ebd4d1137dc6bca56d2b29610f6a4ada5"},"schema_version":"1.0","source":{"id":"2303.12914","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.12914","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"arxiv_version","alias_value":"2303.12914v1","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.12914","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"pith_short_12","alias_value":"QAQNTSW5DWY4","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"pith_short_16","alias_value":"QAQNTSW5DWY4NWYQ","created_at":"2026-07-05T05:53:55Z"},{"alias_kind":"pith_short_8","alias_value":"QAQNTSW5","created_at":"2026-07-05T05:53:55Z"}],"graph_snapshots":[{"event_id":"sha256:93bd988259da9eaddfaee5a9bf1da3b081a3686a4dfcfba4d8cbee3963d1fcf1","target":"graph","created_at":"2026-07-05T05:53:55Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2303.12914/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transformer neural networks are rapidly being integrated into state-of-the-art solutions for natural language processing (NLP) and computer vision. However, the complex structure of these models creates challenges for accelerating their execution on conventional electronic platforms. We propose the first silicon photonic hardware neural network accelerator called TRON for transformer-based models such as BERT, and Vision Transformers. Our analysis demonstrates that TRON exhibits at least 14x better throughput and 8x better energy efficiency, in comparison to state-of-the-art transformer accele","authors_text":"Febin Sunny, Mahdi Nikdast, Salma Afifi, Sudeep Pasricha","cross_cats":["cs.AR"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T21:09:49Z","title":"TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.12914","kind":"arxiv","version":1},"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:bbf485e1ecb76b3e67e220656527654b604e78a32242386cd66b7b16e1f3851b","target":"record","created_at":"2026-07-05T05:53:55Z","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":"f8f0a02b3de2bc9c3d2cf7702914e97c364bfcb7a12c8f9f1adf6578a46bfe40","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T21:09:49Z","title_canon_sha256":"06049c10efd3dad543ce9269ae2a5f0ebd4d1137dc6bca56d2b29610f6a4ada5"},"schema_version":"1.0","source":{"id":"2303.12914","kind":"arxiv","version":1}},"canonical_sha256":"8020d9cadd1db1c6db103dd48700e37cbff0bf1a987791a59286c9b3bc3d7a35","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8020d9cadd1db1c6db103dd48700e37cbff0bf1a987791a59286c9b3bc3d7a35","first_computed_at":"2026-07-05T05:53:55.455868Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:53:55.455868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jEOhBtNkU/PUb58I4k+msVJtC3GU1/hwrvYqHFx7OKAYZd8rBiAXQkEtrHaxjU1dt/je+ZlTQqkFql5cYZvRDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:53:55.456275Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.12914","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bbf485e1ecb76b3e67e220656527654b604e78a32242386cd66b7b16e1f3851b","sha256:93bd988259da9eaddfaee5a9bf1da3b081a3686a4dfcfba4d8cbee3963d1fcf1"],"state_sha256":"1918357e5e059898d83a51eac1efef9c0a6aeda6350c826f05b52c5655f71935"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LJy7l2Xq5la8epnAWc33OshTrtXdwxL5/Mtrv5XzMRkkK4IFMO42o+34onPDUXH7+6DSPi1IZQFWJx09KuECAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T16:18:26.319861Z","bundle_sha256":"281bdea24d5a1f43e9d20c44fd3be5cb8166044b186ded42bd2a79fadd4eaad7"}}