Delta-Rule-based Weight Calibration Method for Low-Power SNN System

In this paper, we propose a Spiking Neural Network (SNN) system using a proposed delta rule algorithm-based calibration method to improve accuracy during hardware implementation. The proposed weight calibration cell employs the pulse-driven computation (PDC) method rather than multiply-accumulation (MAC) to perform low-power operations. The PDC organizes the operational part as a counter that performs multiplication operations by counting the number of pulses over a specified time. Compared with existing MAC-based cells, the proposed weight calibration cell enables real-time weight updates without requiring additional memory access, as the counter is capable of performing both computational and memory functions concurrently. This approach results in a significant energy reduction of 95% to 99% and delivers a high energy efficiency of 155 pJ/Train. Additionally, the system achieves stable calibration accuracy, ranging from 94% to 95%, even across diverse accuracy environments.