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Award ID contains: 2019366

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  1. ; ; (, Lecture notes in computer science)
    Li, Yi; Tahar, Sofiene (Ed.)
    Machine learning accelerators (MLAs) are increasingly im- portant in many applications such as image and video processing, speech recognition, and natural language processing. To achieve the needed per- formances and power efficiencies, MLAs are highly concurrent. The cor- rectness of MLAs hinges on the concept of sequential consistency, i.e., the concurrent execution of a program by an MLA must be equivalent to a sequential execution of the program. In this paper, we certify the sequential consistency of modular MLAs using theorem proving. We őrst provide a formalization of the MLAs and deőne their sequential consis- tency. After that, we introduce our certiőcation methodology based on inductive theorem proving. Finally, we demonstrate the feasibility of our approach through the analysis of the NVIDIA Deep Learning Accelerator and the Versatile Tensor Accelerator 
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    Accepted Manuscript Full Text Available