{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:S5CRFEKAJLSYFJACNN4F24IJEN","short_pith_number":"pith:S5CRFEKA","schema_version":"1.0","canonical_sha256":"97451291404ae582a4026b785d710923584b6099cbc39ce2f5202032ae85fd83","source":{"kind":"arxiv","id":"2409.03460","version":1},"attestation_state":"computed","paper":{"title":"LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christian Micheloni, Matteo Dunnhofer, Moritz Nottebaum","submitted_at":"2024-09-05T12:18:32Z","abstract_excerpt":"Research in efficient vision backbones is evolving into models that are a mixture of convolutions and transformer blocks. A smart combination of both, architecture-wise and component-wise is mandatory to excel in the speedaccuracy trade-off. Most publications focus on maximizing accuracy and utilize MACs (multiply accumulate operations) as an efficiency metric. The latter however often do not measure accurately how fast a model actually is due to factors like memory access cost and degree of parallelism. We analyzed common modules and architectural design choices for backbones not in terms of "},"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":"2409.03460","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-05T12:18:32Z","cross_cats_sorted":[],"title_canon_sha256":"8b6d503a45238725651b66162661b63f8d125acea9acf094c8f185d323e426fb","abstract_canon_sha256":"1c83a2e69009704dcc198fc941ef05795de7d3053920a2f21f377a9c0243c0c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:03:31.713421Z","signature_b64":"XH7qsn7CN3sHtpqA3U19gt9wEKeABeAjjSYpXN20mL04cHrSQOWnZ8hofqC8VPMZ40t7tDPdiwuIDuSVTaznBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97451291404ae582a4026b785d710923584b6099cbc39ce2f5202032ae85fd83","last_reissued_at":"2026-07-05T09:03:31.712914Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:03:31.712914Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christian Micheloni, Matteo Dunnhofer, Moritz Nottebaum","submitted_at":"2024-09-05T12:18:32Z","abstract_excerpt":"Research in efficient vision backbones is evolving into models that are a mixture of convolutions and transformer blocks. A smart combination of both, architecture-wise and component-wise is mandatory to excel in the speedaccuracy trade-off. Most publications focus on maximizing accuracy and utilize MACs (multiply accumulate operations) as an efficiency metric. The latter however often do not measure accurately how fast a model actually is due to factors like memory access cost and degree of parallelism. We analyzed common modules and architectural design choices for backbones not in terms of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.03460","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/2409.03460/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":"2409.03460","created_at":"2026-07-05T09:03:31.712975+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.03460v1","created_at":"2026-07-05T09:03:31.712975+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.03460","created_at":"2026-07-05T09:03:31.712975+00:00"},{"alias_kind":"pith_short_12","alias_value":"S5CRFEKAJLSY","created_at":"2026-07-05T09:03:31.712975+00:00"},{"alias_kind":"pith_short_16","alias_value":"S5CRFEKAJLSYFJAC","created_at":"2026-07-05T09:03:31.712975+00:00"},{"alias_kind":"pith_short_8","alias_value":"S5CRFEKA","created_at":"2026-07-05T09:03:31.712975+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15369","citing_title":"iFormer: Integrating ConvNet and Transformer for Mobile Application","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S5CRFEKAJLSYFJACNN4F24IJEN","json":"https://pith.science/pith/S5CRFEKAJLSYFJACNN4F24IJEN.json","graph_json":"https://pith.science/api/pith-number/S5CRFEKAJLSYFJACNN4F24IJEN/graph.json","events_json":"https://pith.science/api/pith-number/S5CRFEKAJLSYFJACNN4F24IJEN/events.json","paper":"https://pith.science/paper/S5CRFEKA"},"agent_actions":{"view_html":"https://pith.science/pith/S5CRFEKAJLSYFJACNN4F24IJEN","download_json":"https://pith.science/pith/S5CRFEKAJLSYFJACNN4F24IJEN.json","view_paper":"https://pith.science/paper/S5CRFEKA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.03460&json=true","fetch_graph":"https://pith.science/api/pith-number/S5CRFEKAJLSYFJACNN4F24IJEN/graph.json","fetch_events":"https://pith.science/api/pith-number/S5CRFEKAJLSYFJACNN4F24IJEN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S5CRFEKAJLSYFJACNN4F24IJEN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S5CRFEKAJLSYFJACNN4F24IJEN/action/storage_attestation","attest_author":"https://pith.science/pith/S5CRFEKAJLSYFJACNN4F24IJEN/action/author_attestation","sign_citation":"https://pith.science/pith/S5CRFEKAJLSYFJACNN4F24IJEN/action/citation_signature","submit_replication":"https://pith.science/pith/S5CRFEKAJLSYFJACNN4F24IJEN/action/replication_record"}},"created_at":"2026-07-05T09:03:31.712975+00:00","updated_at":"2026-07-05T09:03:31.712975+00:00"}