{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KEBUL7YT27VHWHQKHUDEZSTGL5","short_pith_number":"pith:KEBUL7YT","schema_version":"1.0","canonical_sha256":"510345ff13d7ea7b1e0a3d064cca665f570aef6e410ba7727ee6debf172f1529","source":{"kind":"arxiv","id":"1911.04021","version":2},"attestation_state":"computed","paper":{"title":"DRiLLS: Deep Reinforcement Learning for Logic Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.AI","authors_text":"Abdelrahman Hosny, Mohamed Shalan, Sherief Reda, Soheil Hashemi","submitted_at":"2019-11-11T00:38:39Z","abstract_excerpt":"Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. Efficient design space exploration is challenging due to the exponential number of possible optimization permutations. Therefore, automating the optimization process is necessary. In this work, we propose a novel reinforcement learning-based methodology that navigates the optimization space without human intervention. We demonstrate the training of an Advantage Actor Critic (A2C) agent that seeks to minimize area subject to a timing cons"},"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":"1911.04021","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-11-11T00:38:39Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"955a18ba2feec502b62dd6b1c0cfce9805455d953f8e95714463e05a5b48a7b4","abstract_canon_sha256":"ca90d9586d313ab2592444bf0aa14854cfe70ef1b722cf46a9a93b18f23d6535"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:18:53.559482Z","signature_b64":"BxdWRoV4jvsXO04Cyt2xrLvgQZNT/jfII0e1+l/4d+nzMMDIDUu2Mu8Bs21sFJMvjddwJTf7kccJzPs00dMhDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"510345ff13d7ea7b1e0a3d064cca665f570aef6e410ba7727ee6debf172f1529","last_reissued_at":"2026-07-05T00:18:53.559063Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:18:53.559063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DRiLLS: Deep Reinforcement Learning for Logic Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.AI","authors_text":"Abdelrahman Hosny, Mohamed Shalan, Sherief Reda, Soheil Hashemi","submitted_at":"2019-11-11T00:38:39Z","abstract_excerpt":"Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. Efficient design space exploration is challenging due to the exponential number of possible optimization permutations. Therefore, automating the optimization process is necessary. In this work, we propose a novel reinforcement learning-based methodology that navigates the optimization space without human intervention. We demonstrate the training of an Advantage Actor Critic (A2C) agent that seeks to minimize area subject to a timing cons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.04021","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/1911.04021/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":"1911.04021","created_at":"2026-07-05T00:18:53.559120+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.04021v2","created_at":"2026-07-05T00:18:53.559120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.04021","created_at":"2026-07-05T00:18:53.559120+00:00"},{"alias_kind":"pith_short_12","alias_value":"KEBUL7YT27VH","created_at":"2026-07-05T00:18:53.559120+00:00"},{"alias_kind":"pith_short_16","alias_value":"KEBUL7YT27VHWHQK","created_at":"2026-07-05T00:18:53.559120+00:00"},{"alias_kind":"pith_short_8","alias_value":"KEBUL7YT","created_at":"2026-07-05T00:18:53.559120+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.10912","citing_title":"Mapping Fusion: Improving FPGA Technology Mapping with ASIC Mapper","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KEBUL7YT27VHWHQKHUDEZSTGL5","json":"https://pith.science/pith/KEBUL7YT27VHWHQKHUDEZSTGL5.json","graph_json":"https://pith.science/api/pith-number/KEBUL7YT27VHWHQKHUDEZSTGL5/graph.json","events_json":"https://pith.science/api/pith-number/KEBUL7YT27VHWHQKHUDEZSTGL5/events.json","paper":"https://pith.science/paper/KEBUL7YT"},"agent_actions":{"view_html":"https://pith.science/pith/KEBUL7YT27VHWHQKHUDEZSTGL5","download_json":"https://pith.science/pith/KEBUL7YT27VHWHQKHUDEZSTGL5.json","view_paper":"https://pith.science/paper/KEBUL7YT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.04021&json=true","fetch_graph":"https://pith.science/api/pith-number/KEBUL7YT27VHWHQKHUDEZSTGL5/graph.json","fetch_events":"https://pith.science/api/pith-number/KEBUL7YT27VHWHQKHUDEZSTGL5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KEBUL7YT27VHWHQKHUDEZSTGL5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KEBUL7YT27VHWHQKHUDEZSTGL5/action/storage_attestation","attest_author":"https://pith.science/pith/KEBUL7YT27VHWHQKHUDEZSTGL5/action/author_attestation","sign_citation":"https://pith.science/pith/KEBUL7YT27VHWHQKHUDEZSTGL5/action/citation_signature","submit_replication":"https://pith.science/pith/KEBUL7YT27VHWHQKHUDEZSTGL5/action/replication_record"}},"created_at":"2026-07-05T00:18:53.559120+00:00","updated_at":"2026-07-05T00:18:53.559120+00:00"}