1 citations · 2 across the 6 of their papers we have counts for
13 papers
High fusion computers: The IoTs, edges, data centers, and humans-in-the-loop as a computer
Wanling Gao, Lei Wang, Mingyu Chen +13
Emerging and future applications rely heavily upon systems consisting of Internet of Things (IoT), edges, data centers, and humans-in-the-loop. Significantly different from warehou…
OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings
Yunyou Huang, Nana Wang, Suqin Tang +10
This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of s…
FLBench: A Benchmark Suite for Federated Learning
Yuan Liang, Yange Guo, Yanxia Gong +3
Federated learning is a new machine learning paradigm. The goal is to build a machine learning model from the data sets distributed on multiple devices so-called an isolated data i…
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…
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…