3 papers
cs.CV2026
Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization
Ruichen Ma, Xiaoyang Zhang, Jian Bai +5
The performance of deep spiking neural networks (SNNs) often relies on batch normalization (BN). However, the advanced dynamic BN variants used in state-of-the-art models introduce…
cs.CV2025
I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks
Ruichen Ma, Liwei Meng, Guanchao Qiao +3
Spiking neural networks (SNNs) promise highly energy-efficient computing, but their adoption is hindered by a critical scarcity of event-stream data. This work introduces I2E, an a…
cs.AR2024
A 0.96pJ/SOP, 30.23K-neuron/mm^2 Heterogeneous Neuromorphic Chip With Fullerene-like Interconnection Topology for Edge-AI Computing
P. J. Zhou, Q. Yu, M. Chen +7
Edge-AI computing requires high energy efficiency, low power consumption, and relatively high flexibility and compact area, challenging the AI-chip design. This work presents a 0.9…