15 citations · 47 across the 12 of their papers we have counts for
12 papers
CMLCompiler: A Unified Compiler for Classical Machine Learning
Xu Wen, Wanling Gao, Anzheng Li +3
Classical machine learning (CML) occupies nearly half of machine learning pipelines in production applications. Unfortunately, it fails to utilize the state-of-the-practice devices…
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…
Pinpointing the Memory Behaviors of DNN Training
Jiansong Li, Xiao Dong, Guangli Li +9
The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators. Characterizing the memory behaviors of DNN train…
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…
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…
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…
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…
The Pitfall of Evaluating Performance on Emerging AI Accelerators
Zihan Jiang, Jiansong Li, Jiangfeng Zhan
In recent years, domain-specific hardware has brought significant performance improvements in deep learning (DL). Both industry and academia only focus on throughput when evaluatin…
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…
A Semantic-based Medical Image Fusion Approach
Fanda Fan, Yunyou Huang, Lei Wang +4
It is necessary for clinicians to comprehensively analyze patient information from different sources. Medical image fusion is a promising approach to providing overall information…
BOPS, Not FLOPS! A New Metric and Roofline Performance Model For Datacenter Computing
Lei Wang, Jianfeng Zhan, Wanling Gao +5
For emerging datacenter (in short, DC) workloads, such as online Internet services or offline data analytics, how to evaluate the upper bound performance and provide apple-to-apple…