{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:UNGLABE2LVK4OF2NDQE7DOHCKK","short_pith_number":"pith:UNGLABE2","schema_version":"1.0","canonical_sha256":"a34cb0049a5d55c7174d1c09f1b8e2528619ca7f901c658e9066a6b52e8f0811","source":{"kind":"arxiv","id":"1602.01528","version":2},"attestation_state":"computed","paper":{"title":"EIE: Efficient Inference Engine on Compressed Deep Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.CV","authors_text":"Ardavan Pedram, Huizi Mao, Jing Pu, Mark A. Horowitz, Song Han, William J. Dally, Xingyu Liu","submitted_at":"2016-02-04T01:28:28Z","abstract_excerpt":"State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power.\n  Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and "},"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":"1602.01528","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-04T01:28:28Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"6b117c1944907e26a34fdffd15d0654376407bea48428e13f37bba1e04409a78","abstract_canon_sha256":"91ad699bee2934e35773bab4acc9d957ba1f8770b47bd0875f5e34564630e3d1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:49.766950Z","signature_b64":"lBx16N6U68o+Z6nZv7vsdA+N0asjFQWWXkdF8KHnABUlIM/JRzQ6tty57YNtnScZWVxdZoFBPpLVtmqYJTKLCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a34cb0049a5d55c7174d1c09f1b8e2528619ca7f901c658e9066a6b52e8f0811","last_reissued_at":"2026-05-18T01:15:49.766441Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:49.766441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EIE: Efficient Inference Engine on Compressed Deep Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.CV","authors_text":"Ardavan Pedram, Huizi Mao, Jing Pu, Mark A. Horowitz, Song Han, William J. Dally, Xingyu Liu","submitted_at":"2016-02-04T01:28:28Z","abstract_excerpt":"State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power.\n  Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.01528","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":""},"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":"1602.01528","created_at":"2026-05-18T01:15:49.766507+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.01528v2","created_at":"2026-05-18T01:15:49.766507+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.01528","created_at":"2026-05-18T01:15:49.766507+00:00"},{"alias_kind":"pith_short_12","alias_value":"UNGLABE2LVK4","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_16","alias_value":"UNGLABE2LVK4OF2N","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_8","alias_value":"UNGLABE2","created_at":"2026-05-18T12:30:46.583412+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"1510.00149","citing_title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UNGLABE2LVK4OF2NDQE7DOHCKK","json":"https://pith.science/pith/UNGLABE2LVK4OF2NDQE7DOHCKK.json","graph_json":"https://pith.science/api/pith-number/UNGLABE2LVK4OF2NDQE7DOHCKK/graph.json","events_json":"https://pith.science/api/pith-number/UNGLABE2LVK4OF2NDQE7DOHCKK/events.json","paper":"https://pith.science/paper/UNGLABE2"},"agent_actions":{"view_html":"https://pith.science/pith/UNGLABE2LVK4OF2NDQE7DOHCKK","download_json":"https://pith.science/pith/UNGLABE2LVK4OF2NDQE7DOHCKK.json","view_paper":"https://pith.science/paper/UNGLABE2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.01528&json=true","fetch_graph":"https://pith.science/api/pith-number/UNGLABE2LVK4OF2NDQE7DOHCKK/graph.json","fetch_events":"https://pith.science/api/pith-number/UNGLABE2LVK4OF2NDQE7DOHCKK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UNGLABE2LVK4OF2NDQE7DOHCKK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UNGLABE2LVK4OF2NDQE7DOHCKK/action/storage_attestation","attest_author":"https://pith.science/pith/UNGLABE2LVK4OF2NDQE7DOHCKK/action/author_attestation","sign_citation":"https://pith.science/pith/UNGLABE2LVK4OF2NDQE7DOHCKK/action/citation_signature","submit_replication":"https://pith.science/pith/UNGLABE2LVK4OF2NDQE7DOHCKK/action/replication_record"}},"created_at":"2026-05-18T01:15:49.766507+00:00","updated_at":"2026-05-18T01:15:49.766507+00:00"}