4 citations · 5 across the 2 of their papers we have counts for
6 papers
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
Fei Tang, Wanling Gao, Jianfeng Zhan +30
Earlier-stage evaluations of a new AI architecture/system need affordable benchmarks. Only using a few AI component benchmarks like MLPerfalone in the other stages may lead to misl…
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
Wanling Gao, Fei Tang, Jianfeng Zhan +31
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…
AI-oriented Medical Workload Allocation for Hierarchical Cloud/Edge/Device Computing
Tianshu Hao, Jianfeng Zhan, Kai Hwang +2
In a hierarchically-structured cloud/edge/device computing environment, workload allocation can greatly affect the overall system performance. This paper deals with AI-oriented med…
Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking
Tianshu Hao, Yunyou Huang, Xu Wen +8
In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues. So for…
HPC AI500: A Benchmark Suite for HPC AI Systems
Zihan Jiang, Wanling Gao, Lei Wang +10
In recent years, with the trend of applying deep learning (DL) in high performance scientific computing, the unique characteristics of emerging DL workloads in HPC raise great chal…
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite
Wanling Gao, Jianfeng Zhan, Lei Wang +11
Several fundamental changes in technology indicate domain-specific hardware and software co-design is the only path left. In this context, architecture, system, data management, an…