{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2A6G2DQ477GZASXWLL6ULBGQFE","short_pith_number":"pith:2A6G2DQ4","schema_version":"1.0","canonical_sha256":"d03c6d0e1cffcd904af65afd4584d02923ad4e1bbbed23874d2d46d2f3150279","source":{"kind":"arxiv","id":"2404.00639","version":2},"attestation_state":"computed","paper":{"title":"RL-MUL 2.0: Multiplier Design Optimization with Parallel Deep Reinforcement Learning and Space Reduction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AR","authors_text":"Dongsheng Zuo, Jiadong Zhu, Yikang Ouyang, Yuzhe Ma","submitted_at":"2024-03-31T10:43:33Z","abstract_excerpt":"Multiplication is a fundamental operation in many applications, and multipliers are widely adopted in various circuits. However, optimizing multipliers is challenging due to the extensive design space. In this paper, we propose a multiplier design optimization framework based on reinforcement learning. We utilize matrix and tensor representations for the compressor tree of a multiplier, enabling seamless integration of convolutional neural networks as the agent network. The agent optimizes the multiplier structure using a Pareto-driven reward customized to balance area and delay. Furthermore, "},"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":"2404.00639","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AR","submitted_at":"2024-03-31T10:43:33Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"395ccb464019c3885f5d70f592ab5d8ac1a0ddbada9420d0b51fea0df9718fba","abstract_canon_sha256":"43bbe8fdb34fe2b3d48ba57e7b84b9ed41fc51a8223c99d2298c8a141a66ce68"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:25.835708Z","signature_b64":"LRrjM42Xi7O84Tg5ZAlSH6rwbn9llx3ADPEmqB+5pbB+B0lA5cbyxsDQUMjUjWTRr8DaxF/nuDK9IuVzLJ/hBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d03c6d0e1cffcd904af65afd4584d02923ad4e1bbbed23874d2d46d2f3150279","last_reissued_at":"2026-07-05T09:54:25.835249Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:25.835249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RL-MUL 2.0: Multiplier Design Optimization with Parallel Deep Reinforcement Learning and Space Reduction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AR","authors_text":"Dongsheng Zuo, Jiadong Zhu, Yikang Ouyang, Yuzhe Ma","submitted_at":"2024-03-31T10:43:33Z","abstract_excerpt":"Multiplication is a fundamental operation in many applications, and multipliers are widely adopted in various circuits. However, optimizing multipliers is challenging due to the extensive design space. In this paper, we propose a multiplier design optimization framework based on reinforcement learning. We utilize matrix and tensor representations for the compressor tree of a multiplier, enabling seamless integration of convolutional neural networks as the agent network. The agent optimizes the multiplier structure using a Pareto-driven reward customized to balance area and delay. Furthermore, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.00639","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/2404.00639/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":"2404.00639","created_at":"2026-07-05T09:54:25.835308+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.00639v2","created_at":"2026-07-05T09:54:25.835308+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.00639","created_at":"2026-07-05T09:54:25.835308+00:00"},{"alias_kind":"pith_short_12","alias_value":"2A6G2DQ477GZ","created_at":"2026-07-05T09:54:25.835308+00:00"},{"alias_kind":"pith_short_16","alias_value":"2A6G2DQ477GZASXW","created_at":"2026-07-05T09:54:25.835308+00:00"},{"alias_kind":"pith_short_8","alias_value":"2A6G2DQ4","created_at":"2026-07-05T09:54:25.835308+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/2A6G2DQ477GZASXWLL6ULBGQFE","json":"https://pith.science/pith/2A6G2DQ477GZASXWLL6ULBGQFE.json","graph_json":"https://pith.science/api/pith-number/2A6G2DQ477GZASXWLL6ULBGQFE/graph.json","events_json":"https://pith.science/api/pith-number/2A6G2DQ477GZASXWLL6ULBGQFE/events.json","paper":"https://pith.science/paper/2A6G2DQ4"},"agent_actions":{"view_html":"https://pith.science/pith/2A6G2DQ477GZASXWLL6ULBGQFE","download_json":"https://pith.science/pith/2A6G2DQ477GZASXWLL6ULBGQFE.json","view_paper":"https://pith.science/paper/2A6G2DQ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.00639&json=true","fetch_graph":"https://pith.science/api/pith-number/2A6G2DQ477GZASXWLL6ULBGQFE/graph.json","fetch_events":"https://pith.science/api/pith-number/2A6G2DQ477GZASXWLL6ULBGQFE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2A6G2DQ477GZASXWLL6ULBGQFE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2A6G2DQ477GZASXWLL6ULBGQFE/action/storage_attestation","attest_author":"https://pith.science/pith/2A6G2DQ477GZASXWLL6ULBGQFE/action/author_attestation","sign_citation":"https://pith.science/pith/2A6G2DQ477GZASXWLL6ULBGQFE/action/citation_signature","submit_replication":"https://pith.science/pith/2A6G2DQ477GZASXWLL6ULBGQFE/action/replication_record"}},"created_at":"2026-07-05T09:54:25.835308+00:00","updated_at":"2026-07-05T09:54:25.835308+00:00"}