EMBEDDED STOCHASTIC-COMPUTING ACCELERATOR ARCHITECTURE AND METHOD FOR CONVOLUTIONAL NEURAL NETWORKS

摘要

The disclosed invention provides a novel architecture that reduces the computation time of stochastic computing-based multiplications in the convolutional layers of convolutional neural networks (CNNs). Each convolution in a CNN is composed of numerous multiplications where each input value is multiplied by a weight vector. Subsequent multiplications are performed by multiplying the input and differences of the successive weights. Leveraging this property, disclosed is a differential Multiply-and-Accumulate unit to reduce the time consumed by convolutions in the architecture. The disclosed architecture offers 1.2× increase in speed and 2.7× increase in energy efficiency compared to known convolutional neural networks.