  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.
