{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BFKMGQMDKWFIBKLUAAC2TBSAVB","short_pith_number":"pith:BFKMGQMD","schema_version":"1.0","canonical_sha256":"0954c34183558a80a9740005a98640a84230d3be992238a98da1f880435d4096","source":{"kind":"arxiv","id":"2410.23776","version":1},"attestation_state":"computed","paper":{"title":"Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.NE","eess.AS"],"primary_cat":"cs.SD","authors_text":"Dylan Muir, Mina Khoei, Weijie Ke","submitted_at":"2024-10-31T09:48:12Z","abstract_excerpt":"XyloAudio is a line of ultra-low-power audio inference chips, designed for in- and near-microphone analysis of audio in real-time energy-constrained scenarios. Xylo is designed around a highly efficient integer-logic processor which simulates parameter- and activity-sparse spiking neural networks (SNNs) using a leaky integrate-and-fire (LIF) neuron model. Neurons on Xylo are quantised integer devices operating in synchronous digital CMOS, with neuron and synapse state quantised to 16 bit, and weight parameters quantised to 8 bit. Xylo is tailored for real-time streaming operation, as opposed t"},"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":"2410.23776","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2024-10-31T09:48:12Z","cross_cats_sorted":["cs.NE","eess.AS"],"title_canon_sha256":"052311d8fb2c605249bd9fb129a0ade949c745001cdae14dc2a080b794e75570","abstract_canon_sha256":"a61123c42d87476936dfac936f874afd3f5ca6a9bb1c6bf75da4f82c952ae70d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:29:05.076947Z","signature_b64":"+5Y61xbg2kdaIhqRxTXXItkaEd2Z6HtSidryxThSxPahEX0BHLJvDghBFQ/aNagpCPHnKvjJ5pwtTziUnp8QCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0954c34183558a80a9740005a98640a84230d3be992238a98da1f880435d4096","last_reissued_at":"2026-07-05T09:29:05.076549Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:29:05.076549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.NE","eess.AS"],"primary_cat":"cs.SD","authors_text":"Dylan Muir, Mina Khoei, Weijie Ke","submitted_at":"2024-10-31T09:48:12Z","abstract_excerpt":"XyloAudio is a line of ultra-low-power audio inference chips, designed for in- and near-microphone analysis of audio in real-time energy-constrained scenarios. Xylo is designed around a highly efficient integer-logic processor which simulates parameter- and activity-sparse spiking neural networks (SNNs) using a leaky integrate-and-fire (LIF) neuron model. Neurons on Xylo are quantised integer devices operating in synchronous digital CMOS, with neuron and synapse state quantised to 16 bit, and weight parameters quantised to 8 bit. Xylo is tailored for real-time streaming operation, as opposed t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23776","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/2410.23776/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":"2410.23776","created_at":"2026-07-05T09:29:05.076606+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.23776v1","created_at":"2026-07-05T09:29:05.076606+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23776","created_at":"2026-07-05T09:29:05.076606+00:00"},{"alias_kind":"pith_short_12","alias_value":"BFKMGQMDKWFI","created_at":"2026-07-05T09:29:05.076606+00:00"},{"alias_kind":"pith_short_16","alias_value":"BFKMGQMDKWFIBKLU","created_at":"2026-07-05T09:29:05.076606+00:00"},{"alias_kind":"pith_short_8","alias_value":"BFKMGQMD","created_at":"2026-07-05T09:29:05.076606+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.09599","citing_title":"Energy Aware Development of Neuromorphic Implantables: From Metrics to Action","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BFKMGQMDKWFIBKLUAAC2TBSAVB","json":"https://pith.science/pith/BFKMGQMDKWFIBKLUAAC2TBSAVB.json","graph_json":"https://pith.science/api/pith-number/BFKMGQMDKWFIBKLUAAC2TBSAVB/graph.json","events_json":"https://pith.science/api/pith-number/BFKMGQMDKWFIBKLUAAC2TBSAVB/events.json","paper":"https://pith.science/paper/BFKMGQMD"},"agent_actions":{"view_html":"https://pith.science/pith/BFKMGQMDKWFIBKLUAAC2TBSAVB","download_json":"https://pith.science/pith/BFKMGQMDKWFIBKLUAAC2TBSAVB.json","view_paper":"https://pith.science/paper/BFKMGQMD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.23776&json=true","fetch_graph":"https://pith.science/api/pith-number/BFKMGQMDKWFIBKLUAAC2TBSAVB/graph.json","fetch_events":"https://pith.science/api/pith-number/BFKMGQMDKWFIBKLUAAC2TBSAVB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BFKMGQMDKWFIBKLUAAC2TBSAVB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BFKMGQMDKWFIBKLUAAC2TBSAVB/action/storage_attestation","attest_author":"https://pith.science/pith/BFKMGQMDKWFIBKLUAAC2TBSAVB/action/author_attestation","sign_citation":"https://pith.science/pith/BFKMGQMDKWFIBKLUAAC2TBSAVB/action/citation_signature","submit_replication":"https://pith.science/pith/BFKMGQMDKWFIBKLUAAC2TBSAVB/action/replication_record"}},"created_at":"2026-07-05T09:29:05.076606+00:00","updated_at":"2026-07-05T09:29:05.076606+00:00"}