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In this paper, we present NESTA, a specialized Neural engine thatsignificantly accelerates the computation of convolution layers in a deepconvolutional neural network, while reducing the computational energy. NESTAreformats Convolutions into $3 \times 3$ batches and uses a hierarchy ofHamming Weight Compressors to process each batch. Besides, when processing theconvolution across multiple channels, NESTA, rather than computing the preciseresult of a convolution per channel, quickly computes an approximation of itspartial sum, and a residual value such that if added to the approximate partialsum, generates the accurate output. Then, instead of immediately adding theresidual, it uses (consumes) the residual when processing the next batch in thehamming weight compressors with available capacity. This mechanism shortens thecritical path by avoiding the need to propagate carry signals during each roundof computation and speeds up the convolution of each channel. In the last stageof computation, when the partial sum of the last channel is computed, NESTAterminates by adding the residual bits to the approximate output to generate acorrect result.

NESTA: Hamming Weight Compression-Based Neural Proc. Engine