{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HYZFXZHZ2X6SSSNOWARYCOIJNW","short_pith_number":"pith:HYZFXZHZ","schema_version":"1.0","canonical_sha256":"3e325be4f9d5fd2949aeb0238139096d9e2df6410daff0174e11b7d086362f24","source":{"kind":"arxiv","id":"2406.17483","version":1},"attestation_state":"computed","paper":{"title":"TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient Neuromorphic Processing on Event-based Vision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Alexandra F. Dobrita, Amirreza Yousefzadeh, Cina Arjmand, Guangzhi Tang, Kanishkan Vadivel, Kevin Shidqi, Manolis Sifalakis, Paul Detterer, YingFu Xu","submitted_at":"2024-06-25T12:04:51Z","abstract_excerpt":"Neuromorphic processors are well-suited for efficiently handling sparse events from event-based cameras. However, they face significant challenges in the growth of computing demand and hardware costs as the input resolution increases. This paper proposes the Trainable Region-of-Interest Prediction (TRIP), the first hardware-efficient hard attention framework for event-based vision processing on a neuromorphic processor. Our TRIP framework actively produces low-resolution Region-of-Interest (ROIs) for efficient and accurate classification. The framework exploits sparse events' inherent low info"},"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":"2406.17483","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T12:04:51Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"1fd5516b050dc09cd4a40bc54466c303cbf40a9e8a953e5f0776506071c801bc","abstract_canon_sha256":"cab9cf73d164063b9c866b1f43d62b58b5653a582cd9b9102eecc7e790b6ce87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:35.849308Z","signature_b64":"HmcpL9Ot4XkBpGOXbYEeKTwFsbW8Rfdx4/oXkHPbnWGGBenMAip/bqwfqzMje8ip0+3a+bwIaW/w3tX245VKDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e325be4f9d5fd2949aeb0238139096d9e2df6410daff0174e11b7d086362f24","last_reissued_at":"2026-07-05T08:36:35.848757Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:35.848757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient Neuromorphic Processing on Event-based Vision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Alexandra F. Dobrita, Amirreza Yousefzadeh, Cina Arjmand, Guangzhi Tang, Kanishkan Vadivel, Kevin Shidqi, Manolis Sifalakis, Paul Detterer, YingFu Xu","submitted_at":"2024-06-25T12:04:51Z","abstract_excerpt":"Neuromorphic processors are well-suited for efficiently handling sparse events from event-based cameras. However, they face significant challenges in the growth of computing demand and hardware costs as the input resolution increases. This paper proposes the Trainable Region-of-Interest Prediction (TRIP), the first hardware-efficient hard attention framework for event-based vision processing on a neuromorphic processor. Our TRIP framework actively produces low-resolution Region-of-Interest (ROIs) for efficient and accurate classification. The framework exploits sparse events' inherent low info"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.17483","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/2406.17483/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":"2406.17483","created_at":"2026-07-05T08:36:35.848833+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.17483v1","created_at":"2026-07-05T08:36:35.848833+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.17483","created_at":"2026-07-05T08:36:35.848833+00:00"},{"alias_kind":"pith_short_12","alias_value":"HYZFXZHZ2X6S","created_at":"2026-07-05T08:36:35.848833+00:00"},{"alias_kind":"pith_short_16","alias_value":"HYZFXZHZ2X6SSSNO","created_at":"2026-07-05T08:36:35.848833+00:00"},{"alias_kind":"pith_short_8","alias_value":"HYZFXZHZ","created_at":"2026-07-05T08:36:35.848833+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04817","citing_title":"Spike-TBR: a Noise Resilient Neuromorphic Event Representation","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HYZFXZHZ2X6SSSNOWARYCOIJNW","json":"https://pith.science/pith/HYZFXZHZ2X6SSSNOWARYCOIJNW.json","graph_json":"https://pith.science/api/pith-number/HYZFXZHZ2X6SSSNOWARYCOIJNW/graph.json","events_json":"https://pith.science/api/pith-number/HYZFXZHZ2X6SSSNOWARYCOIJNW/events.json","paper":"https://pith.science/paper/HYZFXZHZ"},"agent_actions":{"view_html":"https://pith.science/pith/HYZFXZHZ2X6SSSNOWARYCOIJNW","download_json":"https://pith.science/pith/HYZFXZHZ2X6SSSNOWARYCOIJNW.json","view_paper":"https://pith.science/paper/HYZFXZHZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.17483&json=true","fetch_graph":"https://pith.science/api/pith-number/HYZFXZHZ2X6SSSNOWARYCOIJNW/graph.json","fetch_events":"https://pith.science/api/pith-number/HYZFXZHZ2X6SSSNOWARYCOIJNW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HYZFXZHZ2X6SSSNOWARYCOIJNW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HYZFXZHZ2X6SSSNOWARYCOIJNW/action/storage_attestation","attest_author":"https://pith.science/pith/HYZFXZHZ2X6SSSNOWARYCOIJNW/action/author_attestation","sign_citation":"https://pith.science/pith/HYZFXZHZ2X6SSSNOWARYCOIJNW/action/citation_signature","submit_replication":"https://pith.science/pith/HYZFXZHZ2X6SSSNOWARYCOIJNW/action/replication_record"}},"created_at":"2026-07-05T08:36:35.848833+00:00","updated_at":"2026-07-05T08:36:35.848833+00:00"}