Machine learning (ML) is successful in achieving human-level artificial
intelligence in various fields. However, it lacks the ability to explain an
outcome due to its black-box nature. While recent efforts on explainable AI
(XAI) has received significant attention, most of the existing solutions are
not applicable in real-time systems since they map interpretability as an
optimization problem, which leads to numerous iterations of time-consuming
complex computations. Although there are existing hardware-based acceleration
framework for XAI, they are implemented through FPGA and designed for specific
tasks, leading to expensive cost and lack of flexibility. In this paper, we
propose a simple yet efficient framework to accelerate various XAI algorithms
with existing hardware accelerators. Specifically, this paper makes three
important contributions. (1) The proposed method is the first attempt in
exploring the effectiveness of Tensor Processing Unit (TPU) to accelerate XAI.
(2) Our proposed solution explores the close relationship between several
existing XAI algorithms with matrix computations, and exploits the synergy
between convolution and Fourier transform, which takes full advantage of TPU's
inherent ability in accelerating matrix computations. (3) Our proposed approach
can lead to real-time outcome interpretation. Extensive experimental evaluation
demonstrates that proposed approach deployed on TPU can provide drastic
improvement in interpretation time (39x on average) as well as energy
efficiency (69x on average) compared to existing acceleration techniques.

本研究提出一种简单且高效的框架，通过存在的硬件加速器来加速各种 XAI 算法，并探索了 Tensor Processing Unit（TPU）加速 XAI 的有效性，利用卷积和傅里叶变换之间的协同关系，充分利用 TPU 在加速矩阵计算方面的本能能力，这种方法可以带来实时结果解释。与现有加速技术相比，基于 TPU 的提议方法可以提供对结果解释时间的巨大改善（平均 39 倍）以及能源效率（平均 69 倍）方面的提升。