Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units

• URL: http://arxiv.org/abs/2103.11927v1
• Date: Mon, 22 Mar 2021 15:11:45 GMT
• Title: Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units
• Authors: Zhixin Pan and Prabhat Mishra
• Abstract summary: We propose a novel framework for accelerating explainable machine learning (ML) using Processing Units (TPUs) The proposed framework exploits the synergy between matrix convolution and Fourier transform, and takes full advantage of TPU's natural ability in accelerating matrix computations. Our proposed approach is applicable across a wide variety of ML algorithms, and effective utilization of TPU-based acceleration can lead to real-time outcome interpretation.
• Score: 3.5027291542274357
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Machine learning (ML) is successful in achieving human-level performance in various fields. However, it lacks the ability to explain an outcome due to its black-box nature. While existing explainable ML is promising, almost all of these methods focus on formatting interpretability as an optimization problem. Such a mapping leads to numerous iterations of time-consuming complex computations, which limits their applicability in real-time applications. In this paper, we propose a novel framework for accelerating explainable ML using Tensor Processing Units (TPUs). The proposed framework exploits the synergy between matrix convolution and Fourier transform, and takes full advantage of TPU's natural ability in accelerating matrix computations. Specifically, this paper makes three important contributions. (1) To the best of our knowledge, our proposed work is the first attempt in enabling hardware acceleration of explainable ML using TPUs. (2) Our proposed approach is applicable across a wide variety of ML algorithms, and effective utilization of TPU-based acceleration can lead to real-time outcome interpretation. (3) Extensive experimental results demonstrate that our proposed approach can provide an order-of-magnitude speedup in both classification time (25x on average) and interpretation time (13x on average) compared to state-of-the-art techniques.

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