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.

翻译：本文提出了一种基于左至右（LR）算术的复合内积计算单元，用于在硬件上加速卷积神经网络（CNN）。所提出的L2R-CIPU方法在VGG-16网络上的有效性已得到验证，并在多种性能指标上进行了评估。与现有架构相比，L2R-CIPU设计实现了1.06倍至6.22倍的性能提升、4.8倍至15倍的TOPS/W能效比以及4.51倍至53.45倍的TOPS/mm²面积效率。