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Poster
A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms
Yu Wang · Gu-Yeon Wei · David Brooks

Mon Mar 02 04:30 PM -- 07:00 PM (PST) @ Ballroom A #32

Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware and software specialization to improve performance. To systematically compare deep learning systems, we introduce a methodology comprised of a set of analysis techniques and parameterized end-to-end models for fully connected, convolutional, and recurrent neural networks. This methodology can be applied to analyze various hardware and software systems, and is intended to complement traditional methods. We demonstrate its utility by comparing two generations of specialized platforms (Google's Cloud TPU v2/v3), three heterogeneous platforms (Google TPU, Nvidia GPU, and Intel CPU), and specialized software stacks (TensorFlow and CUDA).

Author Information

Yu Wang (Harvard University)
Gu-Yeon Wei (Harvard University)
David Brooks (Harvard University)

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