  Convolutional neural network (CNN) achieves excellent performance on
fascinating tasks such as image recognition and natural language processing at
the cost of high power consumption. Stochastic computing (SC) is an attractive
paradigm implemented in low power applications which performs arithmetic
operations with simple logic and low hardware cost. However, conventional
memory structure designed and optimized for binary computing leads to extra
data conversion costs, which significantly decreases the energy efficiency.
Therefore, a new memory system designed for SC-based multiply-accumulate (MAC)
engine applied in CNN which is compatible with conventional memory system is
proposed in this paper. As a result, the overall energy consumption of our new
computing structure is 0.91pJ, which is reduced by 82.1% compared with the
conventional structure, and the energy efficiency achieves 164.8 TOPS/W.
