  This paper explores Google's Edge TPU for implementing a practical network
intrusion detection system (NIDS) at the edge of IoT, based on a deep learning
approach. While there are a significant number of related works that explore
machine learning based NIDS for the IoT edge, they generally do not consider
the issue of the required computational and energy resources. The focus of this
paper is the exploration of deep learning-based NIDS at the edge of IoT, and in
particular the computational and energy efficiency. In particular, the paper
studies Google's Edge TPU as a hardware platform, and considers the following
three key metrics: computation (inference) time, energy efficiency and the
traffic classification performance. Various scaled model sizes of two major
deep neural network architectures are used to investigate these three metrics.
The performance of the Edge TPU-based implementation is compared with that of
an energy efficient embedded CPU (ARM Cortex A53). Our experimental evaluation
shows some unexpected results, such as the fact that the CPU significantly
outperforms the Edge TPU for small model sizes.
