activity
20152022
most citedComparison and Benchmarking of AI Models and Frameworks on Mobile Devices

41 citations · 85 across the 13 of their papers we have counts for

collaborators

23 papers

cs.DC2022

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…

cs.CV20211 cited

Shift-and-Balance Attention

Chunjie Luo, Jianfeng Zhan, Tianshu Hao +2

Attention is an effective mechanism to improve the deep model capability. Squeeze-and-Excite (SE) introduces a light-weight attention branch to enhance the network's representation…

cs.PF20214 cited

HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking

Zihan Jiang, Wanling Gao, Fei Tang +6

Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the tr…

cs.PF202013 cited

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…

cs.LG20201 cited

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…

cs.LG202041 cited

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…