{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:33VHOD6G3IWQB7M67S2UFRPUXM","short_pith_number":"pith:33VHOD6G","schema_version":"1.0","canonical_sha256":"deea770fc6da2d00fd9efcb542c5f4bb070540c13dfbbd953ab1cac3d0b7b05b","source":{"kind":"arxiv","id":"1703.03073","version":1},"attestation_state":"computed","paper":{"title":"Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Liangzhen Lai, Naveen Suda, Vikas Chandra","submitted_at":"2017-03-08T23:49:20Z","abstract_excerpt":"Deep convolutional neural network (CNN) inference requires significant amount of memory and computation, which limits its deployment on embedded devices. To alleviate these problems to some extent, prior research utilize low precision fixed-point numbers to represent the CNN weights and activations. However, the minimum required data precision of fixed-point weights varies across different networks and also across different layers of the same network. In this work, we propose using floating-point numbers for representing the weights and fixed-point numbers for representing the activations. We "},"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":"1703.03073","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-08T23:49:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"b45cec9f5110141ca7568d9f20976fccc55795cddce4a68cce8f2d7c1fc3e4dc","abstract_canon_sha256":"ddf40bded7dbbdceb5061dd2ba27699a9ea25a5c2ed193e1b2d6debe36f38e66"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:01.639548Z","signature_b64":"wgc1nK0fMlRVOm24SdKWffgwX5zL0L+joCJ9POIk3XktZBOBvPm34Ad7fhxfxArexAUMSiPbsv5dlypVY9DdDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"deea770fc6da2d00fd9efcb542c5f4bb070540c13dfbbd953ab1cac3d0b7b05b","last_reissued_at":"2026-05-18T00:49:01.638846Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:01.638846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Liangzhen Lai, Naveen Suda, Vikas Chandra","submitted_at":"2017-03-08T23:49:20Z","abstract_excerpt":"Deep convolutional neural network (CNN) inference requires significant amount of memory and computation, which limits its deployment on embedded devices. To alleviate these problems to some extent, prior research utilize low precision fixed-point numbers to represent the CNN weights and activations. However, the minimum required data precision of fixed-point weights varies across different networks and also across different layers of the same network. In this work, we propose using floating-point numbers for representing the weights and fixed-point numbers for representing the activations. We "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.03073","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":"1703.03073","created_at":"2026-05-18T00:49:01.638951+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.03073v1","created_at":"2026-05-18T00:49:01.638951+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.03073","created_at":"2026-05-18T00:49:01.638951+00:00"},{"alias_kind":"pith_short_12","alias_value":"33VHOD6G3IWQ","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"33VHOD6G3IWQB7M6","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"33VHOD6G","created_at":"2026-05-18T12:30:55.937587+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.11804","citing_title":"Memory- and Communication-Aware Model Compression for Distributed Deep Learning Inference on IoT","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/33VHOD6G3IWQB7M67S2UFRPUXM","json":"https://pith.science/pith/33VHOD6G3IWQB7M67S2UFRPUXM.json","graph_json":"https://pith.science/api/pith-number/33VHOD6G3IWQB7M67S2UFRPUXM/graph.json","events_json":"https://pith.science/api/pith-number/33VHOD6G3IWQB7M67S2UFRPUXM/events.json","paper":"https://pith.science/paper/33VHOD6G"},"agent_actions":{"view_html":"https://pith.science/pith/33VHOD6G3IWQB7M67S2UFRPUXM","download_json":"https://pith.science/pith/33VHOD6G3IWQB7M67S2UFRPUXM.json","view_paper":"https://pith.science/paper/33VHOD6G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.03073&json=true","fetch_graph":"https://pith.science/api/pith-number/33VHOD6G3IWQB7M67S2UFRPUXM/graph.json","fetch_events":"https://pith.science/api/pith-number/33VHOD6G3IWQB7M67S2UFRPUXM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/33VHOD6G3IWQB7M67S2UFRPUXM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/33VHOD6G3IWQB7M67S2UFRPUXM/action/storage_attestation","attest_author":"https://pith.science/pith/33VHOD6G3IWQB7M67S2UFRPUXM/action/author_attestation","sign_citation":"https://pith.science/pith/33VHOD6G3IWQB7M67S2UFRPUXM/action/citation_signature","submit_replication":"https://pith.science/pith/33VHOD6G3IWQB7M67S2UFRPUXM/action/replication_record"}},"created_at":"2026-05-18T00:49:01.638951+00:00","updated_at":"2026-05-18T00:49:01.638951+00:00"}