A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads
A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads
复制标题
针对深度学习工作负载的新型人工智能加速器的综合评估
DOI：
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复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Matthew Boyd
中科院分区：
文献类型：
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作者：
M. Emani;Zhen Xie;Siddhisanket Raskar;Varuni K. Sastry;William Arnold;Bruce Wilson;R. Thakur;V. Vishwanath;Zhengchun Liu;M. Papka;Cindy Orozco Bohorquez;Rickey C. Weisner;Karen Li;Yongning Sheng;Yun Du;Jian Zhang;A. Tsyplikhin;Gurdaman S. Khaira;J. Fowers;R. Sivakumar;Victoria Godsoe;Adrián Macías;Chetan Tekur;Matthew Boyd
科学应用越来越多地采用人工智能(AI)技术来推动科学。高性能计算中心正在评估新兴的新型硬件加速器，以高效运行人工智能驱动的科学应用程序。由于这些系统的硬件架构和软件堆栈千差万别，因此很难理解这些加速器的性能。深度学习工作负载评估的最新进展主要集中在CPU和GPU上。本文综述了SambaNova、Cerebras、Graphcore和Groq等公司基于数据流的新型人工智能加速器。我们提供了对这些加速器的第一次评估，这些加速器具有不同的工作负载，如深度学习(DL)原语、基准模型和科学机器学习应用程序。我们还评估了集体通信的性能，这是分布式下行链路实现的关键，并对扩展效率进行了研究。然后，我们讨论在超级计算系统中集成这些新型人工智能加速器的关键见解、挑战和机遇。
Scientific applications are increasingly adopting Artificial Intelligence (AI) techniques to advance science. High-performance computing centers are evaluating emerging novel hardware accelerators to efficiently run AI-driven science applications. With a wide diversity in the hardware architectures and software stacks of these systems, it is challenging to understand how these accelerators perform. The state-of-the-art in the evaluation of deep learning workloads primarily focuses on CPUs and GPUs. In this paper, we present an overview of dataflow-based novel AI accelerators from SambaNova, Cerebras, Graphcore, and Groq. We present a first-of-a-kind evaluation of these accelerators with diverse workloads, such as Deep Learning (DL) primitives, benchmark models, and scientific machine learning applications. We also evaluate the performance of collective communication, which is key for distributed DL implementation, along with a study of scaling efficiency. We then discuss key insights, challenges, and opportunities in integrating these novel AI accelerators in supercomputing systems.
DOI：
10.1109/micro50266.2020.00058
发表时间：
2020-09
期刊：
2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)
影响因子：
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作者：
Sheng-Chun Kao;Geonhwa Jeong;T. Krishna
通讯作者：
Sheng-Chun Kao;Geonhwa Jeong;T. Krishna
MAESTRO：一种以数据为中心的方法，用于了解 DNN 映射的重用、性能和硬件成本
影响因子：
3.6
作者：
Kwon, Hyoukjun;Chatarasi, Prasanth;Sarkar, Vivek;Krishna, Tushar;Pellauer, Michael;Parashar, Angshuman
通讯作者：
Parashar, Angshuman