Efficient Low-Bit Neural Network With Memristor-Based Reconfigurable Circuits

As neural network models are developed and optimized, the use of neural networks in edge devices is increasing, where low-bit neural networks, such as binary neural networks and mixed-precision neural networks, are ideal for edge AI applications. Peripheral circuits and in-memory computing macro are the main components for deploying low-bit precision neural networks on edge AI. However, existing peripheral circuits, including communication units, control modules and analog-to-digital converters (ADCs), are implemented by software or mixed-signal circuits, resulting in significant power and area overheads. To address this issue, memristor-based reconfigurable circuits are proposed for a fully analog implementation of low-bit neural networks without ADCs. In addition, a memristor-based mixed-precision network with a variety of mixed-precision modes is illustrated to verify the effectiveness of deploying low-bit neural networks on edge devices based on the proposed circuits. Furthermore, hybrid simulation results demonstrate that the proposed memristor-based mixed-precision network achieves 84.8∼87.5 % accuracy on the CIFAR-10 dataset, and the parameter scale of the network model is reduced by 1.6∼20x. The circuit analysis demonstrated that the proposed circuits are accurate, robust, and energy-efficient with varying mixed precision, providing a promising and universal solution for applying low-bit neural networks on edge devices.

Duke Scholars

Author Yiran Chen Electrical and Computer Engineering