{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:B5AWWGQ34JUNWOSLS6TP5VNSH7","short_pith_number":"pith:B5AWWGQ3","canonical_record":{"source":{"id":"2606.27884","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2026-06-26T09:27:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ac9994adae76e9d5e6e9077a3f02418d4120f9dbb03cd80d355be884679e3899","abstract_canon_sha256":"13d143cba88710c74bdd6e0d7a646579c6c11ff5d87bd1d8ba1c325aa77ca260"},"schema_version":"1.0"},"canonical_sha256":"0f416b1a1be268db3a4b97a6fed5b23ff286622140a6caf9780aa9fc588dc89a","source":{"kind":"arxiv","id":"2606.27884","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.27884","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"arxiv_version","alias_value":"2606.27884v1","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27884","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"pith_short_12","alias_value":"B5AWWGQ34JUN","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"pith_short_16","alias_value":"B5AWWGQ34JUNWOSL","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"pith_short_8","alias_value":"B5AWWGQ3","created_at":"2026-06-29T01:14:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:B5AWWGQ34JUNWOSLS6TP5VNSH7","target":"record","payload":{"canonical_record":{"source":{"id":"2606.27884","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2026-06-26T09:27:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ac9994adae76e9d5e6e9077a3f02418d4120f9dbb03cd80d355be884679e3899","abstract_canon_sha256":"13d143cba88710c74bdd6e0d7a646579c6c11ff5d87bd1d8ba1c325aa77ca260"},"schema_version":"1.0"},"canonical_sha256":"0f416b1a1be268db3a4b97a6fed5b23ff286622140a6caf9780aa9fc588dc89a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T01:14:51.661832Z","signature_b64":"TgxXfnYk357/KgKUzM2vQ9MTG0NKTi4Ip+VCDSZNb6Ux+9rPDjRKHd/Fpdfa6929qNLT0+N33bIhw55F3QEcCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f416b1a1be268db3a4b97a6fed5b23ff286622140a6caf9780aa9fc588dc89a","last_reissued_at":"2026-06-29T01:14:51.661441Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T01:14:51.661441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.27884","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":"2026-06-29T01:14:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0wy7qZI2SbpV7MT9zPBa59nCbummvb9GjmcHFplsgz/ZpLaQljWcrI33HpJ0uuTSLmwizDlTtxHkZ54VyT0aAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T20:40:38.248908Z"},"content_sha256":"9fe63c1a5bacef46d7a947ab9ab310980124f204e65c03c27f9aa588bb324b4d","schema_version":"1.0","event_id":"sha256:9fe63c1a5bacef46d7a947ab9ab310980124f204e65c03c27f9aa588bb324b4d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:B5AWWGQ34JUNWOSLS6TP5VNSH7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.AR","authors_text":"Cristina Silvano, Leandro Fiorin, Marco Ronzani","submitted_at":"2026-06-26T09:27:19Z","abstract_excerpt":"Mixed-precision computation has been introduced in deep neural networks (DNNs) as an effective approach to reduce latency, energy consumption, and memory footprint. However, efficiently mapping mixed-precision networks onto multi-precision spatial architectures poses several challenges. These include determining the appropriate precision for each layer, balancing layer-wise accuracy sensitivity to quantization against architectural heterogeneity and system-level constraints, and accurately estimating the system-level cost of heterogeneous precision assignments. This work presents SEADA, an eff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27884","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/2606.27884/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-29T01:14:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ble6Rfs+xEv54mg74y1/loCvMrlUjG5X77Hoelfxt09foYZHbvdouHyqIu+gG31H2WwBCGxjWa9tp/yOSDM4AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T20:40:38.249284Z"},"content_sha256":"6da51d8bce6cd4872f05dfb51a40c24e0595066cc975f78984b4ef29f8de9f1e","schema_version":"1.0","event_id":"sha256:6da51d8bce6cd4872f05dfb51a40c24e0595066cc975f78984b4ef29f8de9f1e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B5AWWGQ34JUNWOSLS6TP5VNSH7/bundle.json","state_url":"https://pith.science/pith/B5AWWGQ34JUNWOSLS6TP5VNSH7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B5AWWGQ34JUNWOSLS6TP5VNSH7/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":"2026-06-29T20:40:38Z","links":{"resolver":"https://pith.science/pith/B5AWWGQ34JUNWOSLS6TP5VNSH7","bundle":"https://pith.science/pith/B5AWWGQ34JUNWOSLS6TP5VNSH7/bundle.json","state":"https://pith.science/pith/B5AWWGQ34JUNWOSLS6TP5VNSH7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B5AWWGQ34JUNWOSLS6TP5VNSH7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:B5AWWGQ34JUNWOSLS6TP5VNSH7","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":"13d143cba88710c74bdd6e0d7a646579c6c11ff5d87bd1d8ba1c325aa77ca260","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2026-06-26T09:27:19Z","title_canon_sha256":"ac9994adae76e9d5e6e9077a3f02418d4120f9dbb03cd80d355be884679e3899"},"schema_version":"1.0","source":{"id":"2606.27884","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.27884","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"arxiv_version","alias_value":"2606.27884v1","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27884","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"pith_short_12","alias_value":"B5AWWGQ34JUN","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"pith_short_16","alias_value":"B5AWWGQ34JUNWOSL","created_at":"2026-06-29T01:14:51Z"},{"alias_kind":"pith_short_8","alias_value":"B5AWWGQ3","created_at":"2026-06-29T01:14:51Z"}],"graph_snapshots":[{"event_id":"sha256:6da51d8bce6cd4872f05dfb51a40c24e0595066cc975f78984b4ef29f8de9f1e","target":"graph","created_at":"2026-06-29T01:14:51Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.27884/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mixed-precision computation has been introduced in deep neural networks (DNNs) as an effective approach to reduce latency, energy consumption, and memory footprint. However, efficiently mapping mixed-precision networks onto multi-precision spatial architectures poses several challenges. These include determining the appropriate precision for each layer, balancing layer-wise accuracy sensitivity to quantization against architectural heterogeneity and system-level constraints, and accurately estimating the system-level cost of heterogeneous precision assignments. This work presents SEADA, an eff","authors_text":"Cristina Silvano, Leandro Fiorin, Marco Ronzani","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2026-06-26T09:27:19Z","title":"SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27884","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":"sha256:9fe63c1a5bacef46d7a947ab9ab310980124f204e65c03c27f9aa588bb324b4d","target":"record","created_at":"2026-06-29T01:14:51Z","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":"13d143cba88710c74bdd6e0d7a646579c6c11ff5d87bd1d8ba1c325aa77ca260","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2026-06-26T09:27:19Z","title_canon_sha256":"ac9994adae76e9d5e6e9077a3f02418d4120f9dbb03cd80d355be884679e3899"},"schema_version":"1.0","source":{"id":"2606.27884","kind":"arxiv","version":1}},"canonical_sha256":"0f416b1a1be268db3a4b97a6fed5b23ff286622140a6caf9780aa9fc588dc89a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f416b1a1be268db3a4b97a6fed5b23ff286622140a6caf9780aa9fc588dc89a","first_computed_at":"2026-06-29T01:14:51.661441Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-29T01:14:51.661441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TgxXfnYk357/KgKUzM2vQ9MTG0NKTi4Ip+VCDSZNb6Ux+9rPDjRKHd/Fpdfa6929qNLT0+N33bIhw55F3QEcCQ==","signature_status":"signed_v1","signed_at":"2026-06-29T01:14:51.661832Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.27884","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9fe63c1a5bacef46d7a947ab9ab310980124f204e65c03c27f9aa588bb324b4d","sha256:6da51d8bce6cd4872f05dfb51a40c24e0595066cc975f78984b4ef29f8de9f1e"],"state_sha256":"34d07dcda687dd23a3d5f1fdedcc1b0f2aebeb06f526eadda8825e98c182ce96"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BIRk0YbUkpCPoQItIrmH4pnv43kui+rpd4wL9jaKfUbdNpzYdeSN8kbPOtWBAkI5OJHpesBkoQwOAMn8WaLwAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T20:40:38.251347Z","bundle_sha256":"7df5467fa57c166cb4b579cb265e67a8b15a3c033b6fc95566b313d7d6f041db"}}