SkippyNN

Abstract

Employing convolutional neural networks (CNNs) in embedded devices seeks novel low-cost and energy efficient CNN accelerators. Stochastic computing (SC) is a promising low-cost alternative to conventional binary implementations of CNNs. Despite the low-cost advantage, SC-based arithmetic units suffer from prohibitive execution time due to processing long bit-streams. In particular, multiplication as the main operation in convolution computation, is an extremely time-consuming operation which hampers employing SC methods in designing embedded CNNs.In this work, we propose a novel architecture, called SkippyNN, that reduces the computation time of SC-based multiplications in the convolutional layers of CNNs. Each convolution in a CNN is composed of numerous multiplications where each input value is multiplied by a weight vector. Producing the result of the first multiplication, the following multiplications can be performed by multiplying the input and the differences of the successive weights. Leveraging this property, we develop a differential Multiply-and-Accumulate unit, called DMAC, to reduce the time consumed by convolutions in SkippyNN. We evaluate the efficiency of SkippyNN using four modern CNNs. On average, SkippyNN offers 1.2x speedup and 2.7x energy saving compared to the binary implementation of CNN accelerators.