{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:DYGAUOY5QAJRQSOZ455ACBBMDC","short_pith_number":"pith:DYGAUOY5","schema_version":"1.0","canonical_sha256":"1e0c0a3b1d80131849d9e77a01042c18a71dbecba7bb18d5a50033b7238149c3","source":{"kind":"arxiv","id":"2205.13045","version":1},"attestation_state":"computed","paper":{"title":"QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AR","authors_text":"Ahmet Inci, Aman Jain, Diana Marculescu, Ruizhou Ding, Siri Garudanagiri Virupaksha, Venkata Vivek Thallam","submitted_at":"2022-05-20T21:05:56Z","abstract_excerpt":"As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantization levels, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements (PE) into the accelerator design space while having accurate and fast power, performance, and area models. In this work, we present QADAM, a highly parameterized quantization-aware power, performance, and area modeling framework for DNN accelerators. Our framework can facilitate future research o"},"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":"2205.13045","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2022-05-20T21:05:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ba3556555ff2592c2f0f19079a5a744db9dcfe2c2ae59254fe5914e03aea626d","abstract_canon_sha256":"6963a0e064850971dfc6eb0d8098c2b2cc54176af59c009dc5535a71e45229c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:26:39.464975Z","signature_b64":"Kyv7rSx75VxfBJYq69wKV3FuSOgcR2NL0MqLYIQo4YeLFZkDm3xPz17Y1A+j+tnrpzCU8GWLKhWRYq6tLfwhCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e0c0a3b1d80131849d9e77a01042c18a71dbecba7bb18d5a50033b7238149c3","last_reissued_at":"2026-07-05T04:26:39.464527Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:26:39.464527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AR","authors_text":"Ahmet Inci, Aman Jain, Diana Marculescu, Ruizhou Ding, Siri Garudanagiri Virupaksha, Venkata Vivek Thallam","submitted_at":"2022-05-20T21:05:56Z","abstract_excerpt":"As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantization levels, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements (PE) into the accelerator design space while having accurate and fast power, performance, and area models. In this work, we present QADAM, a highly parameterized quantization-aware power, performance, and area modeling framework for DNN accelerators. Our framework can facilitate future research o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.13045","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.13045/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":"2205.13045","created_at":"2026-07-05T04:26:39.464587+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.13045v1","created_at":"2026-07-05T04:26:39.464587+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.13045","created_at":"2026-07-05T04:26:39.464587+00:00"},{"alias_kind":"pith_short_12","alias_value":"DYGAUOY5QAJR","created_at":"2026-07-05T04:26:39.464587+00:00"},{"alias_kind":"pith_short_16","alias_value":"DYGAUOY5QAJRQSOZ","created_at":"2026-07-05T04:26:39.464587+00:00"},{"alias_kind":"pith_short_8","alias_value":"DYGAUOY5","created_at":"2026-07-05T04:26:39.464587+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/DYGAUOY5QAJRQSOZ455ACBBMDC","json":"https://pith.science/pith/DYGAUOY5QAJRQSOZ455ACBBMDC.json","graph_json":"https://pith.science/api/pith-number/DYGAUOY5QAJRQSOZ455ACBBMDC/graph.json","events_json":"https://pith.science/api/pith-number/DYGAUOY5QAJRQSOZ455ACBBMDC/events.json","paper":"https://pith.science/paper/DYGAUOY5"},"agent_actions":{"view_html":"https://pith.science/pith/DYGAUOY5QAJRQSOZ455ACBBMDC","download_json":"https://pith.science/pith/DYGAUOY5QAJRQSOZ455ACBBMDC.json","view_paper":"https://pith.science/paper/DYGAUOY5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.13045&json=true","fetch_graph":"https://pith.science/api/pith-number/DYGAUOY5QAJRQSOZ455ACBBMDC/graph.json","fetch_events":"https://pith.science/api/pith-number/DYGAUOY5QAJRQSOZ455ACBBMDC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DYGAUOY5QAJRQSOZ455ACBBMDC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DYGAUOY5QAJRQSOZ455ACBBMDC/action/storage_attestation","attest_author":"https://pith.science/pith/DYGAUOY5QAJRQSOZ455ACBBMDC/action/author_attestation","sign_citation":"https://pith.science/pith/DYGAUOY5QAJRQSOZ455ACBBMDC/action/citation_signature","submit_replication":"https://pith.science/pith/DYGAUOY5QAJRQSOZ455ACBBMDC/action/replication_record"}},"created_at":"2026-07-05T04:26:39.464587+00:00","updated_at":"2026-07-05T04:26:39.464587+00:00"}