{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:QPSXAP6E7Z4M6UVILKCTFTGRI3","short_pith_number":"pith:QPSXAP6E","canonical_record":{"source":{"id":"2301.05126","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2023-01-12T16:28:31Z","cross_cats_sorted":[],"title_canon_sha256":"9100f2a805b565d5e8718f045efe230c26ff2d2762addcdda194f95fb2196f37","abstract_canon_sha256":"f2248901f67a22d07364b4783ca62625dee6c72d1aa06ea30bddaca3f1d6047a"},"schema_version":"1.0"},"canonical_sha256":"83e5703fc4fe78cf52a85a8532ccd146d01db37ac88d83f648c06e3b047d9a77","source":{"kind":"arxiv","id":"2301.05126","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.05126","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"arxiv_version","alias_value":"2301.05126v1","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.05126","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"pith_short_12","alias_value":"QPSXAP6E7Z4M","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"QPSXAP6E7Z4M6UVI","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"QPSXAP6E","created_at":"2026-07-05T05:32:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:QPSXAP6E7Z4M6UVILKCTFTGRI3","target":"record","payload":{"canonical_record":{"source":{"id":"2301.05126","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2023-01-12T16:28:31Z","cross_cats_sorted":[],"title_canon_sha256":"9100f2a805b565d5e8718f045efe230c26ff2d2762addcdda194f95fb2196f37","abstract_canon_sha256":"f2248901f67a22d07364b4783ca62625dee6c72d1aa06ea30bddaca3f1d6047a"},"schema_version":"1.0"},"canonical_sha256":"83e5703fc4fe78cf52a85a8532ccd146d01db37ac88d83f648c06e3b047d9a77","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:32:40.902200Z","signature_b64":"jS9CVbnEziUxs+LYjgDyTuQxruyRaMwIRfaUzPcgkLWPGL6V40syNWx+oi/IgFtQXDFWdBB20+ksh+RBODxeBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83e5703fc4fe78cf52a85a8532ccd146d01db37ac88d83f648c06e3b047d9a77","last_reissued_at":"2026-07-05T05:32:40.901805Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:32:40.901805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.05126","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:32:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8ayN83id8wWUUU49bKaIZaJzubyJTIQ9Y+kZuDpfMyh6YNTRvOQCAV28U0cExwOO1GMC8j3BtAzAskWG1IcbCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:46:47.903210Z"},"content_sha256":"f4778a203d49f54824c082ffdb777fde1df233f0b0498268adc19f34e8631476","schema_version":"1.0","event_id":"sha256:f4778a203d49f54824c082ffdb777fde1df233f0b0498268adc19f34e8631476"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:QPSXAP6E7Z4M6UVILKCTFTGRI3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HEP-BNN: A Framework for Finding Low-Latency Execution Configurations of BNNs on Heterogeneous Multiprocessor Platforms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Ching-Chi Lin, Jian-Jia Chen, Leonard David Bereholschi, Mikail Yayla","submitted_at":"2023-01-12T16:28:31Z","abstract_excerpt":"Binarized Neural Networks (BNNs) significantly reduce the computation and memory demands with binarized weights and activations compared to full-precision NNs. Executing a layer in a BNN on different devices of a heterogeneous multiprocessor platform consisting of CPU and GPU can affect the inference performance, i.e., accuracy and latency. Usually, a heterogeneous HW platform consisting of a CPU and a GPU is available to execute the BNN workloads. However, to use the heterogeneous HW effectively, it is necessary to find an efficient strategy for BNN workload mapping. In this work, we propose "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.05126","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/2301.05126/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:32:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ee3rcP6IzwoluHt6S4xRMax/K8KqaZd94qxzgbcprg6rC3Nazmcu5O0It1Jb5EH41Yr+crKeav/yY6TUHtQQAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:46:47.903724Z"},"content_sha256":"90c512cdfb86f7f8d270f043c22d7a08b79f2af68cb1207d44f7ee00aeeb1ad7","schema_version":"1.0","event_id":"sha256:90c512cdfb86f7f8d270f043c22d7a08b79f2af68cb1207d44f7ee00aeeb1ad7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QPSXAP6E7Z4M6UVILKCTFTGRI3/bundle.json","state_url":"https://pith.science/pith/QPSXAP6E7Z4M6UVILKCTFTGRI3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QPSXAP6E7Z4M6UVILKCTFTGRI3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T15:46:47Z","links":{"resolver":"https://pith.science/pith/QPSXAP6E7Z4M6UVILKCTFTGRI3","bundle":"https://pith.science/pith/QPSXAP6E7Z4M6UVILKCTFTGRI3/bundle.json","state":"https://pith.science/pith/QPSXAP6E7Z4M6UVILKCTFTGRI3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QPSXAP6E7Z4M6UVILKCTFTGRI3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QPSXAP6E7Z4M6UVILKCTFTGRI3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f2248901f67a22d07364b4783ca62625dee6c72d1aa06ea30bddaca3f1d6047a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2023-01-12T16:28:31Z","title_canon_sha256":"9100f2a805b565d5e8718f045efe230c26ff2d2762addcdda194f95fb2196f37"},"schema_version":"1.0","source":{"id":"2301.05126","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.05126","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"arxiv_version","alias_value":"2301.05126v1","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.05126","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"pith_short_12","alias_value":"QPSXAP6E7Z4M","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"QPSXAP6E7Z4M6UVI","created_at":"2026-07-05T05:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"QPSXAP6E","created_at":"2026-07-05T05:32:40Z"}],"graph_snapshots":[{"event_id":"sha256:90c512cdfb86f7f8d270f043c22d7a08b79f2af68cb1207d44f7ee00aeeb1ad7","target":"graph","created_at":"2026-07-05T05:32:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2301.05126/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Binarized Neural Networks (BNNs) significantly reduce the computation and memory demands with binarized weights and activations compared to full-precision NNs. Executing a layer in a BNN on different devices of a heterogeneous multiprocessor platform consisting of CPU and GPU can affect the inference performance, i.e., accuracy and latency. Usually, a heterogeneous HW platform consisting of a CPU and a GPU is available to execute the BNN workloads. However, to use the heterogeneous HW effectively, it is necessary to find an efficient strategy for BNN workload mapping. In this work, we propose ","authors_text":"Ching-Chi Lin, Jian-Jia Chen, Leonard David Bereholschi, Mikail Yayla","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2023-01-12T16:28:31Z","title":"HEP-BNN: A Framework for Finding Low-Latency Execution Configurations of BNNs on Heterogeneous Multiprocessor Platforms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.05126","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f4778a203d49f54824c082ffdb777fde1df233f0b0498268adc19f34e8631476","target":"record","created_at":"2026-07-05T05:32:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f2248901f67a22d07364b4783ca62625dee6c72d1aa06ea30bddaca3f1d6047a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2023-01-12T16:28:31Z","title_canon_sha256":"9100f2a805b565d5e8718f045efe230c26ff2d2762addcdda194f95fb2196f37"},"schema_version":"1.0","source":{"id":"2301.05126","kind":"arxiv","version":1}},"canonical_sha256":"83e5703fc4fe78cf52a85a8532ccd146d01db37ac88d83f648c06e3b047d9a77","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"83e5703fc4fe78cf52a85a8532ccd146d01db37ac88d83f648c06e3b047d9a77","first_computed_at":"2026-07-05T05:32:40.901805Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:32:40.901805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jS9CVbnEziUxs+LYjgDyTuQxruyRaMwIRfaUzPcgkLWPGL6V40syNWx+oi/IgFtQXDFWdBB20+ksh+RBODxeBA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:32:40.902200Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.05126","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f4778a203d49f54824c082ffdb777fde1df233f0b0498268adc19f34e8631476","sha256:90c512cdfb86f7f8d270f043c22d7a08b79f2af68cb1207d44f7ee00aeeb1ad7"],"state_sha256":"543f3113523ef73a8fd8ef02ade658c1b4736006b3690c699c286b36bde2c030"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lI2Vy0veUYquRLLoX1J3mXrAnycnpgDOGpFwhUUv/QAu+4BCYiudTjOxlUS14bV7z4nC2PPy34TP4IeMvpjaDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T15:46:47.909147Z","bundle_sha256":"d2042d31bcf75c029f04c2ef6d021fc7368c6e52cb0e46666a93c37acb966efb"}}