A hardware–software blueprint for flexible deep learning specialization

Thierry Moreau, Tianqi Chen, Luis Vega, Jared Roesch, Eddie Yan, Lianmin Zheng, Josh Fromm, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
July 2019
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Abstract

This article describes the Versatile Tensor Accelerator (VTA), a programmable DL architecture designed to be extensible in the face of evolving workloads. VTA achieves “flexible specialization” via a parameterizable architecture, two-level Instruction Set Architecture (ISA), and a Just in Time (JIT) compiler.

Type
Journal article
Publication
IEEE Micro
Ziheng Jiang
Ziheng Jiang
Ph.D. Student

His research center around co-designing efficient algorithms and systems for machine learning.

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