  Tensor Processing Units (TPUs) are specialized hardware accelerators for deep
learning developed by Google. This paper aims to explore TPUs in cloud and edge
computing focusing on its applications in AI. We provide an overview of TPUs,
their general architecture, specifically their design in relation to neural
networks, compilation techniques and supporting frameworks. Furthermore, we
provide a comparative analysis of Cloud and Edge TPU performance against other
counterpart chip architectures. Our results show that TPUs can provide
significant performance improvements in both cloud and edge computing.
Additionally, this paper underscores the imperative need for further research
in optimization techniques for efficient deployment of AI architectures on the
Edge TPU and benchmarking standards for a more robust comparative analysis in
edge computing scenarios. The primary motivation behind this push for research
is that efficient AI acceleration, facilitated by TPUs, can lead to substantial
savings in terms of time, money, and environmental resources.
