  In this paper, we present NESTA, a specialized Neural engine that
significantly accelerates the computation of convolution layers in a deep
convolutional neural network, while reducing the computational energy. NESTA
reformats Convolutions into $3 \times 3$ batches and uses a hierarchy of
Hamming Weight Compressors to process each batch. Besides, when processing the
convolution across multiple channels, NESTA, rather than computing the precise
result of a convolution per channel, quickly computes an approximation of its
partial sum, and a residual value such that if added to the approximate partial
sum, generates the accurate output. Then, instead of immediately adding the
residual, it uses (consumes) the residual when processing the next batch in the
hamming weight compressors with available capacity. This mechanism shortens the
critical path by avoiding the need to propagate carry signals during each round
of computation and speeds up the convolution of each channel. In the last stage
of computation, when the partial sum of the last channel is computed, NESTA
terminates by adding the residual bits to the approximate output to generate a
correct result.
