{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RBB5COK7MLFGXADX6GP7IK6WUC","short_pith_number":"pith:RBB5COK7","schema_version":"1.0","canonical_sha256":"8843d1395f62ca6b8077f19ff42bd6a0b4aa4cad673378563e5f386458fbcf71","source":{"kind":"arxiv","id":"2310.16757","version":2},"attestation_state":"computed","paper":{"title":"All-rounder: A Flexible AI Accelerator with Diverse Data Format Support and Morphable Structure for Multi-DNN Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Banseok Shin, Jaeha Kung, Sehun Park, Seock-Hwan Noh, Seungpyo Lee, Yongjoo Jang","submitted_at":"2023-10-25T16:45:02Z","abstract_excerpt":"Recognizing the explosive increase in the use of AI-based applications, several industrial companies developed custom ASICs (e.g., Google TPU, IBM RaPiD, Intel NNP-I/NNP-T) and constructed a hyperscale cloud infrastructure with them. These ASICs perform operations of the inference or training process of AI models which are requested by users. Since the AI models have different data formats and types of operations, the ASICs need to support diverse data formats and various operation shapes. However, the previous ASIC solutions do not or less fulfill these requirements. To overcome these limitat"},"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":"2310.16757","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2023-10-25T16:45:02Z","cross_cats_sorted":[],"title_canon_sha256":"8623b6ab01fd115b2ce5a6236f238221d9dd564670b4c6f079df2807008d02e4","abstract_canon_sha256":"68405735d3c50fb040edeb2d5310d917625ac48b53651e89f43425d0eccd76c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:21:16.651508Z","signature_b64":"ITi57ZBQvEQA+V6F1eofTJ5lFPU3T1gm2/kV0KF60ePLFykoXIhDSVD1aMVjibKkt6BfLG1jNfMpt58JIB/9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8843d1395f62ca6b8077f19ff42bd6a0b4aa4cad673378563e5f386458fbcf71","last_reissued_at":"2026-07-05T10:21:16.651034Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:21:16.651034Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"All-rounder: A Flexible AI Accelerator with Diverse Data Format Support and Morphable Structure for Multi-DNN Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Banseok Shin, Jaeha Kung, Sehun Park, Seock-Hwan Noh, Seungpyo Lee, Yongjoo Jang","submitted_at":"2023-10-25T16:45:02Z","abstract_excerpt":"Recognizing the explosive increase in the use of AI-based applications, several industrial companies developed custom ASICs (e.g., Google TPU, IBM RaPiD, Intel NNP-I/NNP-T) and constructed a hyperscale cloud infrastructure with them. These ASICs perform operations of the inference or training process of AI models which are requested by users. Since the AI models have different data formats and types of operations, the ASICs need to support diverse data formats and various operation shapes. However, the previous ASIC solutions do not or less fulfill these requirements. To overcome these limitat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.16757","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.16757/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":"2310.16757","created_at":"2026-07-05T10:21:16.651096+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.16757v2","created_at":"2026-07-05T10:21:16.651096+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.16757","created_at":"2026-07-05T10:21:16.651096+00:00"},{"alias_kind":"pith_short_12","alias_value":"RBB5COK7MLFG","created_at":"2026-07-05T10:21:16.651096+00:00"},{"alias_kind":"pith_short_16","alias_value":"RBB5COK7MLFGXADX","created_at":"2026-07-05T10:21:16.651096+00:00"},{"alias_kind":"pith_short_8","alias_value":"RBB5COK7","created_at":"2026-07-05T10:21:16.651096+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.18065","citing_title":"FlexiBit: Fully Flexible Precision Bit-parallel Accelerator Architecture for Arbitrary Mixed Precision AI","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RBB5COK7MLFGXADX6GP7IK6WUC","json":"https://pith.science/pith/RBB5COK7MLFGXADX6GP7IK6WUC.json","graph_json":"https://pith.science/api/pith-number/RBB5COK7MLFGXADX6GP7IK6WUC/graph.json","events_json":"https://pith.science/api/pith-number/RBB5COK7MLFGXADX6GP7IK6WUC/events.json","paper":"https://pith.science/paper/RBB5COK7"},"agent_actions":{"view_html":"https://pith.science/pith/RBB5COK7MLFGXADX6GP7IK6WUC","download_json":"https://pith.science/pith/RBB5COK7MLFGXADX6GP7IK6WUC.json","view_paper":"https://pith.science/paper/RBB5COK7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.16757&json=true","fetch_graph":"https://pith.science/api/pith-number/RBB5COK7MLFGXADX6GP7IK6WUC/graph.json","fetch_events":"https://pith.science/api/pith-number/RBB5COK7MLFGXADX6GP7IK6WUC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RBB5COK7MLFGXADX6GP7IK6WUC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RBB5COK7MLFGXADX6GP7IK6WUC/action/storage_attestation","attest_author":"https://pith.science/pith/RBB5COK7MLFGXADX6GP7IK6WUC/action/author_attestation","sign_citation":"https://pith.science/pith/RBB5COK7MLFGXADX6GP7IK6WUC/action/citation_signature","submit_replication":"https://pith.science/pith/RBB5COK7MLFGXADX6GP7IK6WUC/action/replication_record"}},"created_at":"2026-07-05T10:21:16.651096+00:00","updated_at":"2026-07-05T10:21:16.651096+00:00"}