@inproceedings{916d54b5b06844beb97ab47fc9da3ce4,
title = "Delta-Rule-based Weight Calibration Method for Low-Power SNN System",
abstract = "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.",
keywords = "Artificial Intelligence, Delta-rule, On-Chip Learning, Online Learning, Pulse-Driven Computation, Spiking Neural Network, Weight Calibration",
author = "Seungjoon Lee and Minkyu Song and Kim, \{Soo Youn\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Symposium on Circuits and Systems, ISCAS 2025 ; Conference date: 25-05-2025 Through 28-05-2025",
year = "2025",
doi = "10.1109/ISCAS56072.2025.11043655",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISCAS 2025 - IEEE International Symposium on Circuits and Systems, Proceedings",
address = "United States",
}
