{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:PZAUZX3ZES5U5RX5AN3OPDKFCM","short_pith_number":"pith:PZAUZX3Z","schema_version":"1.0","canonical_sha256":"7e414cdf7924bb4ec6fd0376e78d45132d72b30b8c3ea77e81961de8fd047746","source":{"kind":"arxiv","id":"1710.08315","version":2},"attestation_state":"computed","paper":{"title":"BENCHIP: Benchmarking Intelligence Processors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.PF","authors_text":"Allen Rush, Cong Liu, Haifeng Liu, Huiying Lan, Jinhua Tao, Lei Zhang, Lingjie Xu, Qi Guo, Shan Tang, Shaoli Liu, Shengyuan Zhou, Tianshi Chen, Willian Chen, Yunji Chen, Zidong Du","submitted_at":"2017-10-23T14:53:54Z","abstract_excerpt":"The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware). However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence proce"},"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":"1710.08315","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PF","submitted_at":"2017-10-23T14:53:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b5796f4ce6178f7956ea16e5468ea7fa37967535aa47ca5fba6c6574205a1588","abstract_canon_sha256":"c5ced76c567b7b6dfbbe45f7e18205fcf877db7ba0dc44fe014711fa3f878f43"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:40.684833Z","signature_b64":"Pz+ubmzxGbE28zjWk0vleCte0HnKtdZ/IDof6h92LSrdF2ZjEtNOIlT1+8h3nD4lREszYQmyBdFxnZq2zzFVBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e414cdf7924bb4ec6fd0376e78d45132d72b30b8c3ea77e81961de8fd047746","last_reissued_at":"2026-05-18T00:29:40.684144Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:40.684144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BENCHIP: Benchmarking Intelligence Processors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.PF","authors_text":"Allen Rush, Cong Liu, Haifeng Liu, Huiying Lan, Jinhua Tao, Lei Zhang, Lingjie Xu, Qi Guo, Shan Tang, Shaoli Liu, Shengyuan Zhou, Tianshi Chen, Willian Chen, Yunji Chen, Zidong Du","submitted_at":"2017-10-23T14:53:54Z","abstract_excerpt":"The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware). However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence proce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08315","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1710.08315","created_at":"2026-05-18T00:29:40.684258+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.08315v2","created_at":"2026-05-18T00:29:40.684258+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08315","created_at":"2026-05-18T00:29:40.684258+00:00"},{"alias_kind":"pith_short_12","alias_value":"PZAUZX3ZES5U","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"PZAUZX3ZES5U5RX5","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"PZAUZX3Z","created_at":"2026-05-18T12:31:37.085036+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PZAUZX3ZES5U5RX5AN3OPDKFCM","json":"https://pith.science/pith/PZAUZX3ZES5U5RX5AN3OPDKFCM.json","graph_json":"https://pith.science/api/pith-number/PZAUZX3ZES5U5RX5AN3OPDKFCM/graph.json","events_json":"https://pith.science/api/pith-number/PZAUZX3ZES5U5RX5AN3OPDKFCM/events.json","paper":"https://pith.science/paper/PZAUZX3Z"},"agent_actions":{"view_html":"https://pith.science/pith/PZAUZX3ZES5U5RX5AN3OPDKFCM","download_json":"https://pith.science/pith/PZAUZX3ZES5U5RX5AN3OPDKFCM.json","view_paper":"https://pith.science/paper/PZAUZX3Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.08315&json=true","fetch_graph":"https://pith.science/api/pith-number/PZAUZX3ZES5U5RX5AN3OPDKFCM/graph.json","fetch_events":"https://pith.science/api/pith-number/PZAUZX3ZES5U5RX5AN3OPDKFCM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PZAUZX3ZES5U5RX5AN3OPDKFCM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PZAUZX3ZES5U5RX5AN3OPDKFCM/action/storage_attestation","attest_author":"https://pith.science/pith/PZAUZX3ZES5U5RX5AN3OPDKFCM/action/author_attestation","sign_citation":"https://pith.science/pith/PZAUZX3ZES5U5RX5AN3OPDKFCM/action/citation_signature","submit_replication":"https://pith.science/pith/PZAUZX3ZES5U5RX5AN3OPDKFCM/action/replication_record"}},"created_at":"2026-05-18T00:29:40.684258+00:00","updated_at":"2026-05-18T00:29:40.684258+00:00"}