{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2A45L7G7ER2WYV7QB2HIGDY6O2","short_pith_number":"pith:2A45L7G7","schema_version":"1.0","canonical_sha256":"d039d5fcdf24756c57f00e8e830f1e769b408ca60b5cada927446d5ceb17e2b2","source":{"kind":"arxiv","id":"2607.18287","version":1},"attestation_state":"computed","paper":{"title":"BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andrea Mattia Garavagno, Antonio Frisoli, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino","submitted_at":"2026-06-30T11:28:46Z","abstract_excerpt":"This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directly onto the sensor die via in-sensor processing. BearingNAS frames the search as a constrained optimization problem targeting extreme micro-budgets (4 to 8 kiB of RAM and 16 to 32 kiB of Flash). To eliminate the reliance on expensive discrete GPUs, we propose a lightweight, derivative-free search strategy paired with a single data-flow search space that leverages a decaying kernel growth formulation to prevent parameter explosion. We evaluate our framework o"},"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":"2607.18287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-30T11:28:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5410eeb40eda307f9eb8455ed24e311d674592a56560f4934efb72122af40b08","abstract_canon_sha256":"dbe491fd3398454f1b3ee2c2e0c592218e26c981fdd244c45c75c466d222ad11"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T00:22:40.194186Z","signature_b64":"xZemuBJ/avRtWmEJx/kGrpFvGg2Zrv28YJ+HNOTg+nzhTg1IOr1725hCsI3RNO7YdHWmoApJvJt1CCyW9tAlAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d039d5fcdf24756c57f00e8e830f1e769b408ca60b5cada927446d5ceb17e2b2","last_reissued_at":"2026-07-22T00:22:40.193429Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T00:22:40.193429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andrea Mattia Garavagno, Antonio Frisoli, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino","submitted_at":"2026-06-30T11:28:46Z","abstract_excerpt":"This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directly onto the sensor die via in-sensor processing. BearingNAS frames the search as a constrained optimization problem targeting extreme micro-budgets (4 to 8 kiB of RAM and 16 to 32 kiB of Flash). To eliminate the reliance on expensive discrete GPUs, we propose a lightweight, derivative-free search strategy paired with a single data-flow search space that leverages a decaying kernel growth formulation to prevent parameter explosion. We evaluate our framework o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18287","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/2607.18287/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.18287","created_at":"2026-07-22T00:22:40.193848+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.18287v1","created_at":"2026-07-22T00:22:40.193848+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18287","created_at":"2026-07-22T00:22:40.193848+00:00"},{"alias_kind":"pith_short_12","alias_value":"2A45L7G7ER2W","created_at":"2026-07-22T00:22:40.193848+00:00"},{"alias_kind":"pith_short_16","alias_value":"2A45L7G7ER2WYV7Q","created_at":"2026-07-22T00:22:40.193848+00:00"},{"alias_kind":"pith_short_8","alias_value":"2A45L7G7","created_at":"2026-07-22T00:22:40.193848+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2A45L7G7ER2WYV7QB2HIGDY6O2","json":"https://pith.science/pith/2A45L7G7ER2WYV7QB2HIGDY6O2.json","graph_json":"https://pith.science/api/pith-number/2A45L7G7ER2WYV7QB2HIGDY6O2/graph.json","events_json":"https://pith.science/api/pith-number/2A45L7G7ER2WYV7QB2HIGDY6O2/events.json","paper":"https://pith.science/paper/2A45L7G7"},"agent_actions":{"view_html":"https://pith.science/pith/2A45L7G7ER2WYV7QB2HIGDY6O2","download_json":"https://pith.science/pith/2A45L7G7ER2WYV7QB2HIGDY6O2.json","view_paper":"https://pith.science/paper/2A45L7G7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.18287&json=true","fetch_graph":"https://pith.science/api/pith-number/2A45L7G7ER2WYV7QB2HIGDY6O2/graph.json","fetch_events":"https://pith.science/api/pith-number/2A45L7G7ER2WYV7QB2HIGDY6O2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2A45L7G7ER2WYV7QB2HIGDY6O2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2A45L7G7ER2WYV7QB2HIGDY6O2/action/storage_attestation","attest_author":"https://pith.science/pith/2A45L7G7ER2WYV7QB2HIGDY6O2/action/author_attestation","sign_citation":"https://pith.science/pith/2A45L7G7ER2WYV7QB2HIGDY6O2/action/citation_signature","submit_replication":"https://pith.science/pith/2A45L7G7ER2WYV7QB2HIGDY6O2/action/replication_record"}},"created_at":"2026-07-22T00:22:40.193848+00:00","updated_at":"2026-07-22T00:22:40.193848+00:00"}