A Survey on Machine Learning Accelerators and Evolutionary Hardware Platforms
A Survey on Machine Learning Accelerators and Evolutionary Hardware Platforms
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机器学习加速器和进化硬件平台调查
DOI：
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发表时间：
2022
影响因子：
2
通讯作者：
Sai Manoj Pudukotai Dinakarrao
中科院分区：
文献类型：
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作者：
Sathwika Bavikadi;Abhijitt Dhavlle;A. Ganguly;Anand Haridass;Hagar Hendy;Cory E. Merkel;V. Reddi;Purab Ranjan Sutradhar;Arun Joseph;Sai Manoj Pudukotai Dinakarrao
长期以来，先进的计算系统一直是人工智能(AI)和机器学习(ML)算法突破的推动者，无论是通过纯粹的计算能力，还是通过形状因素的微型化。然而，随着AI/ML算法变得更加复杂，数据集的规模增加，现有的计算平台不再足以弥合算法创新和硬件设计之间的差距。本文对各种ML加速器进行了综述。
Advanced computing systems have long been enablers for breakthroughs in artificial intelligence (AI) and machine learning (ML) algorithms, either through sheer computational power or form-factor miniaturization. However, as AI/ML algorithms become more complex and the size of data sets increases, existing computing platforms are no longer sufficient to bridge the gap between algorithmic innovation and hardware design. This article presents a survey about various ML accelerators.