  The use of machine learning techniques has significantly increased the
physics discovery potential of neutrino telescopes. In the upcoming years, we
are expecting upgrade of currently existing detectors and new telescopes with
novel experimental hardware, yielding more statistics as well as more
complicated data signals. This calls out for an upgrade on the software side
needed to handle this more complex data in a more efficient way. Specifically,
we seek low power and fast software methods to achieve real-time signal
processing, where current machine learning methods are too expensive to be
deployed in the resource-constrained regions where these experiments are
located. We present the first attempt at and a proof-of-concept for enabling
machine learning methods to be deployed in-detector for water/ice neutrino
telescopes via quantization and deployment on Google Edge Tensor Processing
Units (TPUs). We design a recursive neural network with a residual
convolutional embedding, and adapt a quantization process to deploy the
algorithm on a Google Edge TPU. This algorithm can achieve similar
reconstruction accuracy compared with traditional GPU-based machine learning
solutions while requiring the same amount of power compared with CPU-based
regression solutions, combining the high accuracy and low power advantages and
enabling real-time in-detector machine learning in even the most
power-restricted environments.
