  Advances in multicore processors and accelerators have opened the flood gates
to greater exploration and application of machine learning techniques to a
variety of applications. These advances, along with breakdowns of several
trends including Moore's Law, have prompted an explosion of processors and
accelerators that promise even greater computational and machine learning
capabilities. These processors and accelerators are coming in many forms, from
CPUs and GPUs to ASICs, FPGAs, and dataflow accelerators. This paper surveys
the current state of these processors and accelerators that have been publicly
announced with performance and power consumption numbers. The performance and
power values are plotted on a scatter graph and a number of dimensions and
observations from the trends on this plot are discussed and analyzed. For
instance, there are interesting trends in the plot regarding power consumption,
numerical precision, and inference versus training. We then select and
benchmark two commercially-available low size, weight, and power (SWaP)
accelerators as these processors are the most interesting for embedded and
mobile machine learning inference applications that are most applicable to the
DoD and other SWaP constrained users. We determine how they actually perform
with real-world images and neural network models, compare those results to the
reported performance and power consumption values and evaluate them against an
Intel CPU that is used in some embedded applications.
