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The end of Moores law and Dennard scaling has led to the end of rapid improvement in general-purpose program performance. Machine learning (ML), and in particular deep learning, is an attractive alternative for architects to explore. It has recently revolutionized vision, speech, language understanding, and many other fields, and it promises to help with the grand challenges facing our society. The computation at its core is low-precision linear algebra. Thus, ML is both broad enough to apply to many domains and narrow enough to benefit from domain-specific architectures, such as Googles Tensor Processing Unit (TPU). Moreover, the growth in demand for ML computing exceeds Moores law at its peak, just as it is fading. Hence, ML experts and computer architects must work together to design the computing systems required to deliver on the potential of ML. This article offers motivation, suggestions, and warnings to computer architects on how to best contribute to the ML revolution.
Dean et al. (Tue,) studied this question.