  Edge computing is a popular target for accelerating machine learning
algorithms supporting mobile devices without requiring the communication
latencies to handle them in the cloud. Edge deployments of machine learning
primarily consider traditional concerns such as SWaP constraints (Size, Weight,
and Power) for their installations. However, such metrics are not entirely
sufficient to consider environmental impacts from computing given the
significant contributions from embodied energy and carbon. In this paper we
explore the tradeoffs of convolutional neural network acceleration engines for
both inference and on-line training. In particular, we explore the use of
processing-in-memory (PIM) approaches, mobile GPU accelerators, and recently
released FPGAs, and compare them with novel Racetrack memory PIM. Replacing
PIM-enabled DDR3 with Racetrack memory PIM can recover its embodied energy as
quickly as 1 year. For high activity ratios, mobile GPUs can be more
sustainable but have higher embodied energy to overcome compared to PIM-enabled
Racetrack memory.
