{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:3KAHBHNPP2BBAGYH64NNVUXVNA","short_pith_number":"pith:3KAHBHNP","schema_version":"1.0","canonical_sha256":"da80709daf7e82101b07f71adad2f5680550e0ca5aac2efea6a6e1418ba84baa","source":{"kind":"arxiv","id":"2103.15960","version":3},"attestation_state":"computed","paper":{"title":"Demonstrating Analog Inference on the BrainScaleS-2 Mobile System","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.AR","authors_text":"Arne Emmel, Dan Husmann, Eric M\\\"uller, Falk Leonard Ebert, Johannes Schemmel, Johannes Weis, Joscha Ilmberger, Oliver Breitwieser, Philipp Spilger, Sebastian Billaudelle, Yannik Stradmann","submitted_at":"2021-03-29T21:22:15Z","abstract_excerpt":"We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset. The analog network core of the ASIC is utilized to perform the multiply-accumulate operations of a convolutional deep neural network. At a system power consumption of 5.6W, we measure a total energy consumption of 192uJ for the ASIC and achieve a classification time of 276us per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of (9"},"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":"2103.15960","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AR","submitted_at":"2021-03-29T21:22:15Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"61c99955840a80fae0f525d215ad694b2cc4640e0eee802e0f44493f61ff4704","abstract_canon_sha256":"ea77d49ccd1ab7b565b934c008e5221c62b74f7b1b96ca5f659c9441da53539d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:44.125729Z","signature_b64":"R2JpqpSKTwvfpKBuhXtTzFoFanGQneRc+LdPOALxiVzDgtcRBORlgM7USOHAC/iZfCBKA7QntHu2s/iCICg6CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da80709daf7e82101b07f71adad2f5680550e0ca5aac2efea6a6e1418ba84baa","last_reissued_at":"2026-07-05T11:29:44.125242Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:44.125242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Demonstrating Analog Inference on the BrainScaleS-2 Mobile System","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.AR","authors_text":"Arne Emmel, Dan Husmann, Eric M\\\"uller, Falk Leonard Ebert, Johannes Schemmel, Johannes Weis, Joscha Ilmberger, Oliver Breitwieser, Philipp Spilger, Sebastian Billaudelle, Yannik Stradmann","submitted_at":"2021-03-29T21:22:15Z","abstract_excerpt":"We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset. The analog network core of the ASIC is utilized to perform the multiply-accumulate operations of a convolutional deep neural network. At a system power consumption of 5.6W, we measure a total energy consumption of 192uJ for the ASIC and achieve a classification time of 276us per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of (9"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.15960","kind":"arxiv","version":3},"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/2103.15960/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":"2103.15960","created_at":"2026-07-05T11:29:44.125311+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.15960v3","created_at":"2026-07-05T11:29:44.125311+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.15960","created_at":"2026-07-05T11:29:44.125311+00:00"},{"alias_kind":"pith_short_12","alias_value":"3KAHBHNPP2BB","created_at":"2026-07-05T11:29:44.125311+00:00"},{"alias_kind":"pith_short_16","alias_value":"3KAHBHNPP2BBAGYH","created_at":"2026-07-05T11:29:44.125311+00:00"},{"alias_kind":"pith_short_8","alias_value":"3KAHBHNP","created_at":"2026-07-05T11:29:44.125311+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/3KAHBHNPP2BBAGYH64NNVUXVNA","json":"https://pith.science/pith/3KAHBHNPP2BBAGYH64NNVUXVNA.json","graph_json":"https://pith.science/api/pith-number/3KAHBHNPP2BBAGYH64NNVUXVNA/graph.json","events_json":"https://pith.science/api/pith-number/3KAHBHNPP2BBAGYH64NNVUXVNA/events.json","paper":"https://pith.science/paper/3KAHBHNP"},"agent_actions":{"view_html":"https://pith.science/pith/3KAHBHNPP2BBAGYH64NNVUXVNA","download_json":"https://pith.science/pith/3KAHBHNPP2BBAGYH64NNVUXVNA.json","view_paper":"https://pith.science/paper/3KAHBHNP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.15960&json=true","fetch_graph":"https://pith.science/api/pith-number/3KAHBHNPP2BBAGYH64NNVUXVNA/graph.json","fetch_events":"https://pith.science/api/pith-number/3KAHBHNPP2BBAGYH64NNVUXVNA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3KAHBHNPP2BBAGYH64NNVUXVNA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3KAHBHNPP2BBAGYH64NNVUXVNA/action/storage_attestation","attest_author":"https://pith.science/pith/3KAHBHNPP2BBAGYH64NNVUXVNA/action/author_attestation","sign_citation":"https://pith.science/pith/3KAHBHNPP2BBAGYH64NNVUXVNA/action/citation_signature","submit_replication":"https://pith.science/pith/3KAHBHNPP2BBAGYH64NNVUXVNA/action/replication_record"}},"created_at":"2026-07-05T11:29:44.125311+00:00","updated_at":"2026-07-05T11:29:44.125311+00:00"}