Convolutional Neural Network accelerator based on computing in memory

Abstract:

The implementation scheme of Convolutional Neural Network(CNN) based on Von Neumann architecture is difficult to meet the requirements of high performance and low power consumption. Therefore, a CNN accelerator based on storage-computing integrated architecture is designed. By using the circuit structure of Resistive Random Access Memory(RRAM) to realize the storage-computing integrated architecture, and using efficient data input pipeline and CNN processing unit to process large-scale image data, high-performance digital image recognition is realized. The simulation results show that the CNN accelerator has faster computing capability and its clock frequency can reach 100 MHz; in addition, the area of the structure is 300 742 μm², which is 56.6% of that of the conventional design method. The acceleration module designed in this paper greatly improves the speed and decreases the energy consumption of CNN accelerator. It shows guiding significance for the design of high performance neural network accelerator.