{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4WUJUFAISCPBN3SU5X4UECJBWT","short_pith_number":"pith:4WUJUFAI","schema_version":"1.0","canonical_sha256":"e5a89a1408909e16ee54edf9420921b4c1f6ecb424ce27e74d40d224d6b7fd9b","source":{"kind":"arxiv","id":"2501.09954","version":1},"attestation_state":"computed","paper":{"title":"AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.AR"],"primary_cat":"cs.LG","authors_text":"Akshat Ramachandran, Anirudh Itagi, Jamin Seo, Tushar Krishna, Yu-Chuan Chuang","submitted_at":"2025-01-17T04:57:42Z","abstract_excerpt":"Design space exploration (DSE) plays a crucial role in enabling custom hardware architectures, particularly for emerging applications like AI, where optimized and specialized designs are essential. With the growing complexity of deep neural networks (DNNs) and the introduction of advanced foundational models (FMs), the design space for DNN accelerators is expanding at an exponential rate. Additionally, this space is highly non-uniform and non-convex, making it increasingly difficult to navigate and optimize. Traditional DSE techniques rely on search-based methods, which involve iterative sampl"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.09954","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T04:57:42Z","cross_cats_sorted":["cs.AI","cs.AR"],"title_canon_sha256":"7b9e57618ac5ca8cd4506d7291bd7a70b63d6cc31cdb397669017d7268663461","abstract_canon_sha256":"1d3960beb9e98ae649f5068fadf80423b31549f8ce990911b82e53407295b579"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:13.860416Z","signature_b64":"RfAd64ityDuVIHqq4SzotoJtAvgBAIan8SpZ7JRAAwLHlDxCiNwVU5/TrAz0jCbeXIi4/AY+Eb5D/mHiWNg4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5a89a1408909e16ee54edf9420921b4c1f6ecb424ce27e74d40d224d6b7fd9b","last_reissued_at":"2026-07-05T10:02:13.859873Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:13.859873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.AR"],"primary_cat":"cs.LG","authors_text":"Akshat Ramachandran, Anirudh Itagi, Jamin Seo, Tushar Krishna, Yu-Chuan Chuang","submitted_at":"2025-01-17T04:57:42Z","abstract_excerpt":"Design space exploration (DSE) plays a crucial role in enabling custom hardware architectures, particularly for emerging applications like AI, where optimized and specialized designs are essential. With the growing complexity of deep neural networks (DNNs) and the introduction of advanced foundational models (FMs), the design space for DNN accelerators is expanding at an exponential rate. Additionally, this space is highly non-uniform and non-convex, making it increasingly difficult to navigate and optimize. Traditional DSE techniques rely on search-based methods, which involve iterative sampl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09954","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/2501.09954/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.09954","created_at":"2026-07-05T10:02:13.859931+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.09954v1","created_at":"2026-07-05T10:02:13.859931+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09954","created_at":"2026-07-05T10:02:13.859931+00:00"},{"alias_kind":"pith_short_12","alias_value":"4WUJUFAISCPB","created_at":"2026-07-05T10:02:13.859931+00:00"},{"alias_kind":"pith_short_16","alias_value":"4WUJUFAISCPBN3SU","created_at":"2026-07-05T10:02:13.859931+00:00"},{"alias_kind":"pith_short_8","alias_value":"4WUJUFAI","created_at":"2026-07-05T10:02:13.859931+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.10303","citing_title":"DiffAxE: Diffusion-driven Hardware Accelerator Generation and Design Space Exploration","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4WUJUFAISCPBN3SU5X4UECJBWT","json":"https://pith.science/pith/4WUJUFAISCPBN3SU5X4UECJBWT.json","graph_json":"https://pith.science/api/pith-number/4WUJUFAISCPBN3SU5X4UECJBWT/graph.json","events_json":"https://pith.science/api/pith-number/4WUJUFAISCPBN3SU5X4UECJBWT/events.json","paper":"https://pith.science/paper/4WUJUFAI"},"agent_actions":{"view_html":"https://pith.science/pith/4WUJUFAISCPBN3SU5X4UECJBWT","download_json":"https://pith.science/pith/4WUJUFAISCPBN3SU5X4UECJBWT.json","view_paper":"https://pith.science/paper/4WUJUFAI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.09954&json=true","fetch_graph":"https://pith.science/api/pith-number/4WUJUFAISCPBN3SU5X4UECJBWT/graph.json","fetch_events":"https://pith.science/api/pith-number/4WUJUFAISCPBN3SU5X4UECJBWT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4WUJUFAISCPBN3SU5X4UECJBWT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4WUJUFAISCPBN3SU5X4UECJBWT/action/storage_attestation","attest_author":"https://pith.science/pith/4WUJUFAISCPBN3SU5X4UECJBWT/action/author_attestation","sign_citation":"https://pith.science/pith/4WUJUFAISCPBN3SU5X4UECJBWT/action/citation_signature","submit_replication":"https://pith.science/pith/4WUJUFAISCPBN3SU5X4UECJBWT/action/replication_record"}},"created_at":"2026-07-05T10:02:13.859931+00:00","updated_at":"2026-07-05T10:02:13.859931+00:00"}