{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TPXDEWEKI73IGFWS2CP6JIJSCX","short_pith_number":"pith:TPXDEWEK","schema_version":"1.0","canonical_sha256":"9bee32588a47f68316d2d09fe4a13215e608d2be9e07e084aa16e8f46c4435de","source":{"kind":"arxiv","id":"1811.06017","version":1},"attestation_state":"computed","paper":{"title":"Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Cunxi Yu, Wang Zhou","submitted_at":"2018-11-14T19:17:14Z","abstract_excerpt":"Due to the increasing complexity of Integrated Circuits (ICs) and System-on-Chip (SoC), developing high-quality synthesis flows within a short market time becomes more challenging. We propose a general approach that precisely estimates the Quality-of-Result (QoR), such as delay and area, of unseen synthesis flows for specific designs. The main idea is training a Recurrent Neural Network (RNN) regressor, where the flows are inputs and QoRs are ground truth. The RNN regressor is constructed with Long Short-Term Memory (LSTM) and fully-connected layers. This approach is demonstrated with 1.2 mill"},"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":"1811.06017","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-14T19:17:14Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"392a0478e38f4796d0265e9318fe47b49000eafc764d58c7ce8e2338a444f403","abstract_canon_sha256":"fe93879e6f212fb4775d9c5c7fc7d1dd2b4b1b48ada2ed45b99db10525e77028"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:38.854696Z","signature_b64":"mE/Z4yBG8wf/6RyaIskWR7caQDbVEeZ7QXJMe5V63J4/7zHxCUdSiNmYQ5kfJQIvi+Nw/FXZsfeQ2dDydAgwCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bee32588a47f68316d2d09fe4a13215e608d2be9e07e084aa16e8f46c4435de","last_reissued_at":"2026-05-18T00:00:38.854089Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:38.854089Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Cunxi Yu, Wang Zhou","submitted_at":"2018-11-14T19:17:14Z","abstract_excerpt":"Due to the increasing complexity of Integrated Circuits (ICs) and System-on-Chip (SoC), developing high-quality synthesis flows within a short market time becomes more challenging. We propose a general approach that precisely estimates the Quality-of-Result (QoR), such as delay and area, of unseen synthesis flows for specific designs. The main idea is training a Recurrent Neural Network (RNN) regressor, where the flows are inputs and QoRs are ground truth. The RNN regressor is constructed with Long Short-Term Memory (LSTM) and fully-connected layers. This approach is demonstrated with 1.2 mill"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06017","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":"1811.06017","created_at":"2026-05-18T00:00:38.854179+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.06017v1","created_at":"2026-05-18T00:00:38.854179+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06017","created_at":"2026-05-18T00:00:38.854179+00:00"},{"alias_kind":"pith_short_12","alias_value":"TPXDEWEKI73I","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"TPXDEWEKI73IGFWS","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"TPXDEWEK","created_at":"2026-05-18T12:32:56.356000+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/TPXDEWEKI73IGFWS2CP6JIJSCX","json":"https://pith.science/pith/TPXDEWEKI73IGFWS2CP6JIJSCX.json","graph_json":"https://pith.science/api/pith-number/TPXDEWEKI73IGFWS2CP6JIJSCX/graph.json","events_json":"https://pith.science/api/pith-number/TPXDEWEKI73IGFWS2CP6JIJSCX/events.json","paper":"https://pith.science/paper/TPXDEWEK"},"agent_actions":{"view_html":"https://pith.science/pith/TPXDEWEKI73IGFWS2CP6JIJSCX","download_json":"https://pith.science/pith/TPXDEWEKI73IGFWS2CP6JIJSCX.json","view_paper":"https://pith.science/paper/TPXDEWEK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.06017&json=true","fetch_graph":"https://pith.science/api/pith-number/TPXDEWEKI73IGFWS2CP6JIJSCX/graph.json","fetch_events":"https://pith.science/api/pith-number/TPXDEWEKI73IGFWS2CP6JIJSCX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TPXDEWEKI73IGFWS2CP6JIJSCX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TPXDEWEKI73IGFWS2CP6JIJSCX/action/storage_attestation","attest_author":"https://pith.science/pith/TPXDEWEKI73IGFWS2CP6JIJSCX/action/author_attestation","sign_citation":"https://pith.science/pith/TPXDEWEKI73IGFWS2CP6JIJSCX/action/citation_signature","submit_replication":"https://pith.science/pith/TPXDEWEKI73IGFWS2CP6JIJSCX/action/replication_record"}},"created_at":"2026-05-18T00:00:38.854179+00:00","updated_at":"2026-05-18T00:00:38.854179+00:00"}