  This paper proposes a composite inner-product computation unit based on
left-to-right (LR) arithmetic for the acceleration of convolution neural
networks (CNN) on hardware. The efficacy of the proposed L2R-CIPU method has
been shown on the VGG-16 network, and assessment is done on various performance
metrics. The L2R-CIPU design achieves 1.06x to 6.22x greater performance, 4.8x
to 15x more TOPS/W, and 4.51x to 53.45x higher TOPS/mm2 than prior
architectures.
