no code implementations • 27 May 2024 • Guihua Zhao, Yating Peng, Jiaxin Zhu, Xin Tang, Zhiyi Yu
This letter proposes an in-sensor computing multiply-and-accumulate (MAC) circuit based on capacitance.
no code implementations • 15 Nov 2023 • Yue Liu, Shanlin Xiao, Bo Li, Zhiyi Yu
As the third-generation neural network, the Spiking Neural Network (SNN) has the advantages of low power consumption and high energy efficiency, making it suitable for implementation on edge devices.
no code implementations • 7 Sep 2023 • Jilong Luo, Shanlin Xiao, Yinsheng Chen, Zhiyi Yu
In this study, we introduce the Current Mean Decoding (CMD) method, which solves the regression problem to facilitate the training of deep SNNs for object detection tasks.
no code implementations • 19 Oct 2020 • Xingyu Yang, Mingyuan Meng, Shanlin Xiao, Zhiyi Yu
Spiking neural networks (SNNs) receive widespread attention because of their low-power hardware characteristic and brain-like signal response mechanism, but currently, the performance of SNNs is still behind Artificial Neural Networks (ANNs).
1 code implementation • 29 Jan 2020 • Mingyuan Meng, Xingyu Yang, Shanlin Xiao, Zhiyi Yu
This module is trained through STDP-based competitive learning and outperforms the baseline modules on learning capability, learning efficiency, and robustness.
1 code implementation • 2 Dec 2019 • Mingyuan Meng, Xingyu Yang, Lei Bi, Jinman Kim, Shanlin Xiao, Zhiyi Yu
Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture and used a sub-optimal vote-based scheme for spike decoding.