{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:GFPP2Z3FTGYTQH66PCDHXDABBL","short_pith_number":"pith:GFPP2Z3F","canonical_record":{"source":{"id":"1712.01192","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2017-12-04T16:54:12Z","cross_cats_sorted":[],"title_canon_sha256":"777bbec3bedc8220772dd8ee4cd7a53a3d8895db6fc2fb0272be95e45b777567","abstract_canon_sha256":"1abe6bd717fd2631eaa023ab79943decc255462b42bb4b7138b6d3502ca03c32"},"schema_version":"1.0"},"canonical_sha256":"315efd676599b1381fde78867b8c010ac070d62f8b8b62695c03fc22ca3fbf38","source":{"kind":"arxiv","id":"1712.01192","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.01192","created_at":"2026-05-18T00:28:57Z"},{"alias_kind":"arxiv_version","alias_value":"1712.01192v1","created_at":"2026-05-18T00:28:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.01192","created_at":"2026-05-18T00:28:57Z"},{"alias_kind":"pith_short_12","alias_value":"GFPP2Z3FTGYT","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"GFPP2Z3FTGYTQH66","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"GFPP2Z3F","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:GFPP2Z3FTGYTQH66PCDHXDABBL","target":"record","payload":{"canonical_record":{"source":{"id":"1712.01192","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2017-12-04T16:54:12Z","cross_cats_sorted":[],"title_canon_sha256":"777bbec3bedc8220772dd8ee4cd7a53a3d8895db6fc2fb0272be95e45b777567","abstract_canon_sha256":"1abe6bd717fd2631eaa023ab79943decc255462b42bb4b7138b6d3502ca03c32"},"schema_version":"1.0"},"canonical_sha256":"315efd676599b1381fde78867b8c010ac070d62f8b8b62695c03fc22ca3fbf38","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:57.695027Z","signature_b64":"dZM+qtpVj65Y7HVsXBlLCp0H2NGOVlxT1j0C3hKzQW38fWooWNvnQzF4pDZN02JpU7neOF93jL06tr+oobnCBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"315efd676599b1381fde78867b8c010ac070d62f8b8b62695c03fc22ca3fbf38","last_reissued_at":"2026-05-18T00:28:57.694476Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:57.694476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.01192","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:28:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rMwiMk+0bvmCx8pqP3nmR9mvgXmntgknM8ksYQKiDgkEIWuRxCcqKU6mLncC2g4PuSsQPx7XQLx2pNLrZgVbAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T00:02:19.231996Z"},"content_sha256":"646b098472ae51c3d78236d40cdb1e1ccae00185028928b755b08679a72bb8dd","schema_version":"1.0","event_id":"sha256:646b098472ae51c3d78236d40cdb1e1ccae00185028928b755b08679a72bb8dd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:GFPP2Z3FTGYTQH66PCDHXDABBL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mixed-precision training of deep neural networks using computational memory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.ET","authors_text":"Abu Sebastian, Bipin Rajendran, Evangelos Eleftheriou, Irem Boybat, Manuel Le Gallo, Nandakumar S. R.","submitted_at":"2017-12-04T16:54:12Z","abstract_excerpt":"Deep neural networks have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image and speech recognition. Training of large DNNs, however, is a computationally intensive task, and this necessitates the development of novel computing architectures targeting this application. A computational memory unit where resistive memory devices are organized in crossbar arrays can be used to locally store the synaptic weights in their conductance states. The expensive multiply accumulate operations can be performed in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.01192","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:28:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K6oJIiUuTYdQn+0jCI4P0N1cy4FzEMkbx+7BB3OMGg1yaNIukiT7KHIiscZpsVktwSoQ7xoArDqc20PjQqb5CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T00:02:19.232524Z"},"content_sha256":"42c3aa7a173b5536bc884d4719c33dc686b3479eb63e646283ee7054e98cd7a6","schema_version":"1.0","event_id":"sha256:42c3aa7a173b5536bc884d4719c33dc686b3479eb63e646283ee7054e98cd7a6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GFPP2Z3FTGYTQH66PCDHXDABBL/bundle.json","state_url":"https://pith.science/pith/GFPP2Z3FTGYTQH66PCDHXDABBL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GFPP2Z3FTGYTQH66PCDHXDABBL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T00:02:19Z","links":{"resolver":"https://pith.science/pith/GFPP2Z3FTGYTQH66PCDHXDABBL","bundle":"https://pith.science/pith/GFPP2Z3FTGYTQH66PCDHXDABBL/bundle.json","state":"https://pith.science/pith/GFPP2Z3FTGYTQH66PCDHXDABBL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GFPP2Z3FTGYTQH66PCDHXDABBL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:GFPP2Z3FTGYTQH66PCDHXDABBL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1abe6bd717fd2631eaa023ab79943decc255462b42bb4b7138b6d3502ca03c32","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2017-12-04T16:54:12Z","title_canon_sha256":"777bbec3bedc8220772dd8ee4cd7a53a3d8895db6fc2fb0272be95e45b777567"},"schema_version":"1.0","source":{"id":"1712.01192","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.01192","created_at":"2026-05-18T00:28:57Z"},{"alias_kind":"arxiv_version","alias_value":"1712.01192v1","created_at":"2026-05-18T00:28:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.01192","created_at":"2026-05-18T00:28:57Z"},{"alias_kind":"pith_short_12","alias_value":"GFPP2Z3FTGYT","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"GFPP2Z3FTGYTQH66","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"GFPP2Z3F","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:42c3aa7a173b5536bc884d4719c33dc686b3479eb63e646283ee7054e98cd7a6","target":"graph","created_at":"2026-05-18T00:28:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Deep neural networks have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image and speech recognition. Training of large DNNs, however, is a computationally intensive task, and this necessitates the development of novel computing architectures targeting this application. A computational memory unit where resistive memory devices are organized in crossbar arrays can be used to locally store the synaptic weights in their conductance states. The expensive multiply accumulate operations can be performed in ","authors_text":"Abu Sebastian, Bipin Rajendran, Evangelos Eleftheriou, Irem Boybat, Manuel Le Gallo, Nandakumar S. R.","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2017-12-04T16:54:12Z","title":"Mixed-precision training of deep neural networks using computational memory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.01192","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:646b098472ae51c3d78236d40cdb1e1ccae00185028928b755b08679a72bb8dd","target":"record","created_at":"2026-05-18T00:28:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1abe6bd717fd2631eaa023ab79943decc255462b42bb4b7138b6d3502ca03c32","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2017-12-04T16:54:12Z","title_canon_sha256":"777bbec3bedc8220772dd8ee4cd7a53a3d8895db6fc2fb0272be95e45b777567"},"schema_version":"1.0","source":{"id":"1712.01192","kind":"arxiv","version":1}},"canonical_sha256":"315efd676599b1381fde78867b8c010ac070d62f8b8b62695c03fc22ca3fbf38","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"315efd676599b1381fde78867b8c010ac070d62f8b8b62695c03fc22ca3fbf38","first_computed_at":"2026-05-18T00:28:57.694476Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:28:57.694476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dZM+qtpVj65Y7HVsXBlLCp0H2NGOVlxT1j0C3hKzQW38fWooWNvnQzF4pDZN02JpU7neOF93jL06tr+oobnCBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:28:57.695027Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.01192","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:646b098472ae51c3d78236d40cdb1e1ccae00185028928b755b08679a72bb8dd","sha256:42c3aa7a173b5536bc884d4719c33dc686b3479eb63e646283ee7054e98cd7a6"],"state_sha256":"8734ba57f9e89bcb2cb844846791e3d815d10315c7ac61858c9d4c482d64e3fc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oTbwgS1O9HckkAWpza+bl2f+xJe7h8Ke+GUdev4XaI0YQyB6yTsx+FUQ/bbwb13TAU8daq+x5WWtChwu7uZDDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T00:02:19.235338Z","bundle_sha256":"3f51e10e68547c9308f9f6361843f2ea1abf04af8257ab6841c376f5b5419d38"}}