{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:OLQQBV6VP2OPG53U3X6P2FPE5F","short_pith_number":"pith:OLQQBV6V","schema_version":"1.0","canonical_sha256":"72e100d7d57e9cf37774ddfcfd15e4e979615b1c2acaa06b2789f365c53a475e","source":{"kind":"arxiv","id":"2205.14833","version":1},"attestation_state":"computed","paper":{"title":"Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.DC","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Bin Liu, Bin Zou, Chaoyue Niu, Chengfei Lv, Congyu Huang, Fan Wu, Fei Wu, Guihai Chen, Guohuan Xu, Hui Shu, Jinde Song, Panos Huang, Peng Lan, Qiulin Yao, Renjie Gu, Shaojie Tang, Tao Huang, Xiaotang Jiang, Zhaode Wang, Ziqi Wu","submitted_at":"2022-05-30T03:43:35Z","abstract_excerpt":"To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system, called Walle, as the foundation. Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration. Specifically, the compute container is based on Mobile Neural Network (MNN), a tensor compute engine "},"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":"2205.14833","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-30T03:43:35Z","cross_cats_sorted":["cs.DC","cs.SY","eess.SY"],"title_canon_sha256":"1ec360a5d96d0d1bb2e2a1bfb72866e77eb661340c7ca76b3de26115bac46690","abstract_canon_sha256":"e9bb5c02dc65f15bb1a2a8e8c6bc8fabe063523e516b871c0fbd157168de6a92"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:27:21.716596Z","signature_b64":"jQq/rwuHYpG7yUw0ntbEgFIce7DlN2DWjzDwaM/Gz9n/+4oXyjmvpWb4DMfH1N+ZiD4iWvSAi3uu2ctSEzJYAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72e100d7d57e9cf37774ddfcfd15e4e979615b1c2acaa06b2789f365c53a475e","last_reissued_at":"2026-07-05T04:27:21.715888Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:27:21.715888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.DC","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Bin Liu, Bin Zou, Chaoyue Niu, Chengfei Lv, Congyu Huang, Fan Wu, Fei Wu, Guihai Chen, Guohuan Xu, Hui Shu, Jinde Song, Panos Huang, Peng Lan, Qiulin Yao, Renjie Gu, Shaojie Tang, Tao Huang, Xiaotang Jiang, Zhaode Wang, Ziqi Wu","submitted_at":"2022-05-30T03:43:35Z","abstract_excerpt":"To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system, called Walle, as the foundation. Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration. Specifically, the compute container is based on Mobile Neural Network (MNN), a tensor compute engine "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.14833","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/2205.14833/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":"2205.14833","created_at":"2026-07-05T04:27:21.715980+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.14833v1","created_at":"2026-07-05T04:27:21.715980+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.14833","created_at":"2026-07-05T04:27:21.715980+00:00"},{"alias_kind":"pith_short_12","alias_value":"OLQQBV6VP2OP","created_at":"2026-07-05T04:27:21.715980+00:00"},{"alias_kind":"pith_short_16","alias_value":"OLQQBV6VP2OPG53U","created_at":"2026-07-05T04:27:21.715980+00:00"},{"alias_kind":"pith_short_8","alias_value":"OLQQBV6V","created_at":"2026-07-05T04:27:21.715980+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/OLQQBV6VP2OPG53U3X6P2FPE5F","json":"https://pith.science/pith/OLQQBV6VP2OPG53U3X6P2FPE5F.json","graph_json":"https://pith.science/api/pith-number/OLQQBV6VP2OPG53U3X6P2FPE5F/graph.json","events_json":"https://pith.science/api/pith-number/OLQQBV6VP2OPG53U3X6P2FPE5F/events.json","paper":"https://pith.science/paper/OLQQBV6V"},"agent_actions":{"view_html":"https://pith.science/pith/OLQQBV6VP2OPG53U3X6P2FPE5F","download_json":"https://pith.science/pith/OLQQBV6VP2OPG53U3X6P2FPE5F.json","view_paper":"https://pith.science/paper/OLQQBV6V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.14833&json=true","fetch_graph":"https://pith.science/api/pith-number/OLQQBV6VP2OPG53U3X6P2FPE5F/graph.json","fetch_events":"https://pith.science/api/pith-number/OLQQBV6VP2OPG53U3X6P2FPE5F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OLQQBV6VP2OPG53U3X6P2FPE5F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OLQQBV6VP2OPG53U3X6P2FPE5F/action/storage_attestation","attest_author":"https://pith.science/pith/OLQQBV6VP2OPG53U3X6P2FPE5F/action/author_attestation","sign_citation":"https://pith.science/pith/OLQQBV6VP2OPG53U3X6P2FPE5F/action/citation_signature","submit_replication":"https://pith.science/pith/OLQQBV6VP2OPG53U3X6P2FPE5F/action/replication_record"}},"created_at":"2026-07-05T04:27:21.715980+00:00","updated_at":"2026-07-05T04:27:21.715980+00:00"}