171 citations · 182 across the 3 of their papers we have counts for
6 papers
Residual Attention Net for Superior Cross-Domain Time Sequence Modeling
Seth H. Huang, Xu Lingjie, Jiang Congwei
We present a novel architecture, residual attention net (RAN), which merges a sequence architecture, universal transformer, and a computer vision architecture, residual net, with a…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
AI Matrix: A Deep Learning Benchmark for Alibaba Data Centers
Wei Zhang, Wei Wei, Lingjie Xu +2
Alibaba has China's largest e-commerce platform. To support its diverse businesses, Alibaba has its own large-scale data centers providing the computing foundation for a wide varie…
XSP: Across-Stack Profiling and Analysis of Machine Learning Models on GPUs
Cheng Li, Abdul Dakkak, Jinjun Xiong +3
There has been a rapid proliferation of machine learning/deep learning (ML) models and wide adoption of them in many application domains. This has made profiling and characterizati…
AI Matrix - Synthetic Benchmarks for DNN
Wei Wei, Lingjie Xu, Lingling Jin +2
Deep neural network (DNN) architectures, such as convolutional neural networks (CNN), involve heavy computation and require hardware, such as CPU, GPU, and AI accelerators, to prov…
BENCHIP: Benchmarking Intelligence Processors
Jinhua Tao, Zidong Du, Qi Guo +12
The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard…