41 citations · 175 across the 26 of their papers we have counts for
7 papers · 1 filter
HPC AI500: The Methodology, Tools, Roofline Performance Models, and Metrics for Benchmarking HPC AI Systems
Zihan Jiang, Lei Wang, Xingwang Xiong +6
The recent years witness a trend of applying large-scale distributed deep learning in both business and scientific computing areas, whose goal is to speed up the training time to a…
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks
Chunjie Luo, Jianfeng Zhan, Lei Wang +1
To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN). Different from Batch Normalization (BN), which normalizes the final summation…
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
Chunjie Luo, Xiwen He, Jianfeng Zhan +3
Due to increasing amounts of data and compute resources, deep learning achieves many successes in various domains. The application of deep learning on the mobile and embedded devic…
AIBench Scenario: Scenario-distilling AI Benchmarking
Wanling Gao, Fei Tang, Jianfeng Zhan +7
Modern real-world application scenarios like Internet services consist of a diversity of AI and non-AI modules with huge code sizes and long and complicated execution paths, which…
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…
Extended Batch Normalization
Chunjie Luo, Jianfeng Zhan, Lei Wang +1
Batch normalization (BN) has become a standard technique for training the modern deep networks. However, its effectiveness diminishes when the batch size becomes smaller, since the…