  We present a 3.1 POp/s/W fully digital hardware accelerator for ternary
neural networks. CUTIE, the Completely Unrolled Ternary Inference Engine,
focuses on minimizing non-computational energy and switching activity so that
dynamic power spent on storing (locally or globally) intermediate results is
minimized. This is achieved by 1) a data path architecture completely unrolled
in the feature map and filter dimensions to reduce switching activity by
favoring silencing over iterative computation and maximizing data re-use, 2)
targeting ternary neural networks which, in contrast to binary NNs, allow for
sparse weights which reduce switching activity, and 3) introducing an optimized
training method for higher sparsity of the filter weights, resulting in a
further reduction of the switching activity. Compared with state-of-the-art
accelerators, CUTIE achieves greater or equal accuracy while decreasing the
overall core inference energy cost by a factor of 4.8x-21x.
