AAAI 2026technical0 citations
I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks
Ruichen Ma, Liwei Meng, Guanchao Qiao, Ning Ning, Yang Liu, Shaogang Hu
Abstract
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 algorithmic framework that resolves this bottleneck by converting static images into high-fidelity event streams. By simulating microsaccadic eye movements with a highly parallelized convolution, I2E achieves a conversion speed over 300x faster than prior methods, uniquely enabling on-the-fly data augmentation for SNN training. The framework
BibTeX
@inproceedings{aaai2026_i2erealtimeimage,
title = {I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks},
author = {Ruichen Ma and Liwei Meng and Guanchao Qiao and Ning Ning and Yang Liu and Shaogang Hu},
booktitle = {AAAI 2026},
year = {2026}
}