Web: http://arxiv.org/abs/2105.12842

Jan. 26, 2022, 2:11 a.m. | Dan Zhang, Safeen Huda, Ebrahim Songhori, Kartik Prabhu, Quoc Le, Anna Goldie, Azalia Mirhoseini

cs.LG updates on arXiv.org arxiv.org

The rapidly-changing deep learning landscape presents a unique opportunity
for building inference accelerators optimized for specific datacenter-scale
workloads. We propose Full-stack Accelerator Search Technique (FAST), a
hardware accelerator search framework that defines a broad optimization
environment covering key design decisions within the hardware-software stack,
including hardware datapath, software scheduling, and compiler passes such as
operation fusion and tensor padding. In this paper, we analyze bottlenecks in
state-of-the-art vision and natural language processing (NLP) models, including
EfficientNet and BERT, and use …

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