{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Y2ATCNE2J2RPL37XJXJA7QKULF","short_pith_number":"pith:Y2ATCNE2","schema_version":"1.0","canonical_sha256":"c68131349a4ea2f5eff74dd20fc154595ef52a9f64cdd20cf2f3f9c64e7a0044","source":{"kind":"arxiv","id":"1902.00478","version":1},"attestation_state":"computed","paper":{"title":"Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Ghasem Pasandi, Massoud Pedram, Shahin Nazarian","submitted_at":"2019-02-01T17:53:17Z","abstract_excerpt":"Approximate Logic Synthesis (ALS) is the process of synthesizing and mapping a given Boolean network to a library of logic cells so that the magnitude/rate of error between outputs of the approximate and initial (exact) Boolean netlists is bounded from above by a predetermined total error threshold. In this paper, we present Q-ALS, a novel framework for ALS with focus on the technology mapping phase. Q-ALS incorporates reinforcement learning and utilizes Boolean difference calculus to estimate the maximum error rate that each node of the given network can tolerate such that the total error rat"},"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":"1902.00478","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2019-02-01T17:53:17Z","cross_cats_sorted":[],"title_canon_sha256":"a46002f3f227df72580574386e381e6915da8e4bd9d1fadf1a8dd682445e991e","abstract_canon_sha256":"dbed01372b4339c6c915b398a78176cb8273040687a5b476d0631b0ea259ab35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:57.393434Z","signature_b64":"1+bbAbA0SuozGwAL1QH0OcCnTVR420MSgF1ITGQqfuI5Jm18BVUtMnsO9KOHwZhK+cIJMefq2Jm4K78mNSiNCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c68131349a4ea2f5eff74dd20fc154595ef52a9f64cdd20cf2f3f9c64e7a0044","last_reissued_at":"2026-05-17T23:54:57.392815Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:57.392815Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Ghasem Pasandi, Massoud Pedram, Shahin Nazarian","submitted_at":"2019-02-01T17:53:17Z","abstract_excerpt":"Approximate Logic Synthesis (ALS) is the process of synthesizing and mapping a given Boolean network to a library of logic cells so that the magnitude/rate of error between outputs of the approximate and initial (exact) Boolean netlists is bounded from above by a predetermined total error threshold. In this paper, we present Q-ALS, a novel framework for ALS with focus on the technology mapping phase. Q-ALS incorporates reinforcement learning and utilizes Boolean difference calculus to estimate the maximum error rate that each node of the given network can tolerate such that the total error rat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.00478","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":""},"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":"1902.00478","created_at":"2026-05-17T23:54:57.392902+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.00478v1","created_at":"2026-05-17T23:54:57.392902+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.00478","created_at":"2026-05-17T23:54:57.392902+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y2ATCNE2J2RP","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y2ATCNE2J2RPL37X","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y2ATCNE2","created_at":"2026-05-18T12:33:33.725879+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/Y2ATCNE2J2RPL37XJXJA7QKULF","json":"https://pith.science/pith/Y2ATCNE2J2RPL37XJXJA7QKULF.json","graph_json":"https://pith.science/api/pith-number/Y2ATCNE2J2RPL37XJXJA7QKULF/graph.json","events_json":"https://pith.science/api/pith-number/Y2ATCNE2J2RPL37XJXJA7QKULF/events.json","paper":"https://pith.science/paper/Y2ATCNE2"},"agent_actions":{"view_html":"https://pith.science/pith/Y2ATCNE2J2RPL37XJXJA7QKULF","download_json":"https://pith.science/pith/Y2ATCNE2J2RPL37XJXJA7QKULF.json","view_paper":"https://pith.science/paper/Y2ATCNE2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.00478&json=true","fetch_graph":"https://pith.science/api/pith-number/Y2ATCNE2J2RPL37XJXJA7QKULF/graph.json","fetch_events":"https://pith.science/api/pith-number/Y2ATCNE2J2RPL37XJXJA7QKULF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y2ATCNE2J2RPL37XJXJA7QKULF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y2ATCNE2J2RPL37XJXJA7QKULF/action/storage_attestation","attest_author":"https://pith.science/pith/Y2ATCNE2J2RPL37XJXJA7QKULF/action/author_attestation","sign_citation":"https://pith.science/pith/Y2ATCNE2J2RPL37XJXJA7QKULF/action/citation_signature","submit_replication":"https://pith.science/pith/Y2ATCNE2J2RPL37XJXJA7QKULF/action/replication_record"}},"created_at":"2026-05-17T23:54:57.392902+00:00","updated_at":"2026-05-17T23:54:57.392902+00:00"}