5 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network
Ruichen Ma, Guanchao Qiao, Yian Liu +4
Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However, advanced binary architectures s…