{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:M5H2AVTENS3OGA2MPV5NDNF34M","short_pith_number":"pith:M5H2AVTE","schema_version":"1.0","canonical_sha256":"674fa056646cb6e3034c7d7ad1b4bbe31d28e4fced629101d595ff8182a2b9eb","source":{"kind":"arxiv","id":"1910.07492","version":1},"attestation_state":"computed","paper":{"title":"Neural Network Design for Energy-Autonomous AI Applications using Temporal Encoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.SP","authors_text":"Alex Yakovlev, Fei Xia, Rishad Shafik, Sergey Mileiko, Shidhartha Das, Thanasin Bunnam","submitted_at":"2019-10-15T16:34:50Z","abstract_excerpt":"Neural Networks (NNs) are steering a new generation of artificial intelligence (AI) applications at the micro-edge. Examples include wireless sensors, wearables and cybernetic systems that collect data and process them to support real-world decisions and controls. For energy autonomy, these applications are typically powered by energy harvesters. As harvesters and other power sources which provide energy autonomy inevitably have power variations, the circuits need to robustly operate over a dynamic power envelope. In other words, the NN hardware needs to be able to function correctly under unp"},"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":"1910.07492","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-10-15T16:34:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f3c5493e63da34c3ec991b0a96a933d126daf5f570de8c19ad174dad5012dc3a","abstract_canon_sha256":"1fb6d82e5eb0d210b691154052b4475bcaeb39248fc7bd31ab7029350e4a239f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:23:03.702540Z","signature_b64":"0YhgxlGaVkh3C1DtkTL0K4ZepgIoweqoUP8q7lTwvURobyCawcud2EhE8yM1WnLGJhGAKbqzeH2TOf5rY7EXDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"674fa056646cb6e3034c7d7ad1b4bbe31d28e4fced629101d595ff8182a2b9eb","last_reissued_at":"2026-07-05T02:23:03.702130Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:23:03.702130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Network Design for Energy-Autonomous AI Applications using Temporal Encoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.SP","authors_text":"Alex Yakovlev, Fei Xia, Rishad Shafik, Sergey Mileiko, Shidhartha Das, Thanasin Bunnam","submitted_at":"2019-10-15T16:34:50Z","abstract_excerpt":"Neural Networks (NNs) are steering a new generation of artificial intelligence (AI) applications at the micro-edge. Examples include wireless sensors, wearables and cybernetic systems that collect data and process them to support real-world decisions and controls. For energy autonomy, these applications are typically powered by energy harvesters. As harvesters and other power sources which provide energy autonomy inevitably have power variations, the circuits need to robustly operate over a dynamic power envelope. In other words, the NN hardware needs to be able to function correctly under unp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.07492","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/1910.07492/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":"1910.07492","created_at":"2026-07-05T02:23:03.702185+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.07492v1","created_at":"2026-07-05T02:23:03.702185+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.07492","created_at":"2026-07-05T02:23:03.702185+00:00"},{"alias_kind":"pith_short_12","alias_value":"M5H2AVTENS3O","created_at":"2026-07-05T02:23:03.702185+00:00"},{"alias_kind":"pith_short_16","alias_value":"M5H2AVTENS3OGA2M","created_at":"2026-07-05T02:23:03.702185+00:00"},{"alias_kind":"pith_short_8","alias_value":"M5H2AVTE","created_at":"2026-07-05T02:23:03.702185+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/M5H2AVTENS3OGA2MPV5NDNF34M","json":"https://pith.science/pith/M5H2AVTENS3OGA2MPV5NDNF34M.json","graph_json":"https://pith.science/api/pith-number/M5H2AVTENS3OGA2MPV5NDNF34M/graph.json","events_json":"https://pith.science/api/pith-number/M5H2AVTENS3OGA2MPV5NDNF34M/events.json","paper":"https://pith.science/paper/M5H2AVTE"},"agent_actions":{"view_html":"https://pith.science/pith/M5H2AVTENS3OGA2MPV5NDNF34M","download_json":"https://pith.science/pith/M5H2AVTENS3OGA2MPV5NDNF34M.json","view_paper":"https://pith.science/paper/M5H2AVTE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.07492&json=true","fetch_graph":"https://pith.science/api/pith-number/M5H2AVTENS3OGA2MPV5NDNF34M/graph.json","fetch_events":"https://pith.science/api/pith-number/M5H2AVTENS3OGA2MPV5NDNF34M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M5H2AVTENS3OGA2MPV5NDNF34M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M5H2AVTENS3OGA2MPV5NDNF34M/action/storage_attestation","attest_author":"https://pith.science/pith/M5H2AVTENS3OGA2MPV5NDNF34M/action/author_attestation","sign_citation":"https://pith.science/pith/M5H2AVTENS3OGA2MPV5NDNF34M/action/citation_signature","submit_replication":"https://pith.science/pith/M5H2AVTENS3OGA2MPV5NDNF34M/action/replication_record"}},"created_at":"2026-07-05T02:23:03.702185+00:00","updated_at":"2026-07-05T02:23:03.702185+00:00"}