{"paper":{"title":"AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.PF","authors_text":"Biao Wang, Chen Zheng, Chongkang Tan, Chuanxin Lan, Chunjie Luo, Daoyi Zheng, Defei Kong, Fanda Fan, Fan Zhang, Fei Tang, Gang Lu, Hainan Ye, Haoning Tang, Huan Li, Jiahui Dai, Jianan Chen, Jianfeng Zhan, Junchao Shao, Kunlin Zhan, Lei Wang, Mengjia Du, Minghe Yu, Rui Ren, Tianshu Hao, Wanling Gao, Xiaoyu Wang, Xinhui Tian, Xiongwang Xiong, Xu Wen, Yatao Li, Yunyou Huang, Zheng Cao, Zhenyu Wang, Zihan Jiang","submitted_at":"2020-02-17T07:29:05Z","abstract_excerpt":"Domain-specific software and hardware co-design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain-specific benchmarking speeds up the process as it provides not only relevant design inputs but also relevant metrics, and tools. Unfortunately, modern workloads like Big data, AI, and Internet services dwarf the traditional one in terms of code size, deployment scale, and execution path, and hence raise serious benchmarking challenges.\n  This paper proposes an agile domain-specific benchmarking methodology. Together with seventeen industry partners, we identif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.07162","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/2002.07162/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"}