An Efficient Stochastic Convolution Architecture Based on Fast FIR Algorithm

Abstract

By utilizing stochastic computing (SC), the hardware consumption of convolutional neural networks (CNNs) can be decreased significantly. However, long stream length is required to produce acceptable results, which leads to extended computation time. As a result, the inherent random fluctuation error and long latency of processing random bitstreams have made previous SC-CNN implementations inefficient compared with conventional binary designs. To address these issues, in this brief, an efficient convolution architecture based on fast FIR algorithm (FFA) is proposed by employing FFA to reduce the computational complexity. Further, the combination of two-line SC and Sobol sequences is applied to decrease the processing cycles. The functional simulation targeting LeNet-5 with MNIST dataset and RTL synthesis results show that the proposed design yields higher area efficiency than previous SC-based ones and achieves 64%, 11% higher efficiency in area and energy compared to the 5-bit fixed-point design while maintaining comparable accuracy.