{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:XRM7RSE7ORWBDPW7RKS7LTBIXU","short_pith_number":"pith:XRM7RSE7","canonical_record":{"source":{"id":"1801.06274","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-19T02:42:10Z","cross_cats_sorted":["cs.AR","cs.NE"],"title_canon_sha256":"6646e1618cd703c2c106b845e9ba55be99c1fa6bae30117547274f661d80934f","abstract_canon_sha256":"d60d6920fefc27f7afcff95889b58b8fef8e048b73c5eba28ec59c0b2863e5e8"},"schema_version":"1.0"},"canonical_sha256":"bc59f8c89f746c11bedf8aa5f5cc28bd24e111ffb8730cd0b9832e44678e5959","source":{"kind":"arxiv","id":"1801.06274","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.06274","created_at":"2026-05-18T00:24:33Z"},{"alias_kind":"arxiv_version","alias_value":"1801.06274v2","created_at":"2026-05-18T00:24:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.06274","created_at":"2026-05-18T00:24:33Z"},{"alias_kind":"pith_short_12","alias_value":"XRM7RSE7ORWB","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XRM7RSE7ORWBDPW7","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XRM7RSE7","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:XRM7RSE7ORWBDPW7RKS7LTBIXU","target":"record","payload":{"canonical_record":{"source":{"id":"1801.06274","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-19T02:42:10Z","cross_cats_sorted":["cs.AR","cs.NE"],"title_canon_sha256":"6646e1618cd703c2c106b845e9ba55be99c1fa6bae30117547274f661d80934f","abstract_canon_sha256":"d60d6920fefc27f7afcff95889b58b8fef8e048b73c5eba28ec59c0b2863e5e8"},"schema_version":"1.0"},"canonical_sha256":"bc59f8c89f746c11bedf8aa5f5cc28bd24e111ffb8730cd0b9832e44678e5959","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:33.218475Z","signature_b64":"TUs38RVP4YAo0nb6CJQF5aQFNMc7BMUCjvxbgVLW91oruehlGtDG/pysxVSW3eiMPCDFoYwx5qHzhX5Aqex/CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc59f8c89f746c11bedf8aa5f5cc28bd24e111ffb8730cd0b9832e44678e5959","last_reissued_at":"2026-05-18T00:24:33.218022Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:33.218022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.06274","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3+D07xhHYciyngj9wBuVHdK8h2JrwM1E6mDRWlJ32SRfncNxxQYT3+Re5kv+nOAXCZJ6tSLEEIHEjp1ax1JTBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:46:41.005907Z"},"content_sha256":"579eba4a29fd45950778e632132ffd8f5e80aaf762432f340f37e1d6f3073f72","schema_version":"1.0","event_id":"sha256:579eba4a29fd45950778e632132ffd8f5e80aaf762432f340f37e1d6f3073f72"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:XRM7RSE7ORWBDPW7RKS7LTBIXU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.NE"],"primary_cat":"cs.LG","authors_text":"Matthew Mattina, Paul Whatmough, Yuhao Zhu","submitted_at":"2018-01-19T02:42:10Z","abstract_excerpt":"Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customized hardware for machine learning algorithms, especially neural networks, to improve compute efficiency. However, machine learning is typically just one processing stage in complex end-to-end applications, involving multiple components in a mobile Systems-on-a-chip (SoC). Focusing only on ML accelerators loses bigger optimization opportunity at the system (SoC) level. This paper argues that hardware architects should"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.06274","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v5Dkl0P+w7/bu3A/RCDwX0oUuvlsBPE1K+KsNLZeCkAqam16btlxjAtwzBJVjBPL9nNO0c/WcNqQrKzu/c/2AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:46:41.006729Z"},"content_sha256":"e582a6698af23f53abed358ad82e1314153ca73cf533671323c90e3e2454acba","schema_version":"1.0","event_id":"sha256:e582a6698af23f53abed358ad82e1314153ca73cf533671323c90e3e2454acba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XRM7RSE7ORWBDPW7RKS7LTBIXU/bundle.json","state_url":"https://pith.science/pith/XRM7RSE7ORWBDPW7RKS7LTBIXU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XRM7RSE7ORWBDPW7RKS7LTBIXU/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":"2026-06-07T22:46:41Z","links":{"resolver":"https://pith.science/pith/XRM7RSE7ORWBDPW7RKS7LTBIXU","bundle":"https://pith.science/pith/XRM7RSE7ORWBDPW7RKS7LTBIXU/bundle.json","state":"https://pith.science/pith/XRM7RSE7ORWBDPW7RKS7LTBIXU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XRM7RSE7ORWBDPW7RKS7LTBIXU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:XRM7RSE7ORWBDPW7RKS7LTBIXU","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":"d60d6920fefc27f7afcff95889b58b8fef8e048b73c5eba28ec59c0b2863e5e8","cross_cats_sorted":["cs.AR","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-19T02:42:10Z","title_canon_sha256":"6646e1618cd703c2c106b845e9ba55be99c1fa6bae30117547274f661d80934f"},"schema_version":"1.0","source":{"id":"1801.06274","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.06274","created_at":"2026-05-18T00:24:33Z"},{"alias_kind":"arxiv_version","alias_value":"1801.06274v2","created_at":"2026-05-18T00:24:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.06274","created_at":"2026-05-18T00:24:33Z"},{"alias_kind":"pith_short_12","alias_value":"XRM7RSE7ORWB","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XRM7RSE7ORWBDPW7","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XRM7RSE7","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:e582a6698af23f53abed358ad82e1314153ca73cf533671323c90e3e2454acba","target":"graph","created_at":"2026-05-18T00:24:33Z","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":"Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customized hardware for machine learning algorithms, especially neural networks, to improve compute efficiency. However, machine learning is typically just one processing stage in complex end-to-end applications, involving multiple components in a mobile Systems-on-a-chip (SoC). Focusing only on ML accelerators loses bigger optimization opportunity at the system (SoC) level. This paper argues that hardware architects should","authors_text":"Matthew Mattina, Paul Whatmough, Yuhao Zhu","cross_cats":["cs.AR","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-19T02:42:10Z","title":"Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.06274","kind":"arxiv","version":2},"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":"sha256:579eba4a29fd45950778e632132ffd8f5e80aaf762432f340f37e1d6f3073f72","target":"record","created_at":"2026-05-18T00:24:33Z","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":"d60d6920fefc27f7afcff95889b58b8fef8e048b73c5eba28ec59c0b2863e5e8","cross_cats_sorted":["cs.AR","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-19T02:42:10Z","title_canon_sha256":"6646e1618cd703c2c106b845e9ba55be99c1fa6bae30117547274f661d80934f"},"schema_version":"1.0","source":{"id":"1801.06274","kind":"arxiv","version":2}},"canonical_sha256":"bc59f8c89f746c11bedf8aa5f5cc28bd24e111ffb8730cd0b9832e44678e5959","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc59f8c89f746c11bedf8aa5f5cc28bd24e111ffb8730cd0b9832e44678e5959","first_computed_at":"2026-05-18T00:24:33.218022Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:33.218022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TUs38RVP4YAo0nb6CJQF5aQFNMc7BMUCjvxbgVLW91oruehlGtDG/pysxVSW3eiMPCDFoYwx5qHzhX5Aqex/CQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:33.218475Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.06274","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:579eba4a29fd45950778e632132ffd8f5e80aaf762432f340f37e1d6f3073f72","sha256:e582a6698af23f53abed358ad82e1314153ca73cf533671323c90e3e2454acba"],"state_sha256":"64fda564126867c1688458681787e6398beb6e8cf2ab9380e4cf5c9a6a1befe1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AEf96BmV9A2oolwW40YO3Zz3O8+MHcqv3u+Ba6YIxK01LIY8QATwH278ztBoI8IofbPoHo0EIFPaZj9VGGn0Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T22:46:41.010306Z","bundle_sha256":"f4d80a5c643143f581d24b198260fe289afbeba40ec81eccd0f04e2335f9b2d3"}}