I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks

• URL: http://arxiv.org/abs/2511.08065v1
• Date: Wed, 12 Nov 2025 01:37:50 GMT
• Title: I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks
• Authors: Ruichen Ma, Liwei Meng, Guanchao Qiao, Ning Ning, Yang Liu, Shaogang Hu,
• Abstract summary: Spiking neural networks (SNNs) promise highly energy-efficient computing, but their adoption is hindered by a critical scarcity of event-stream data.<n>This work introduces I2E, an algorithmic framework that resolves this bottleneck by converting static images into high-fidelity event streams.<n>I2E achieves a conversion speed over 300x faster than prior methods, enabling on-the-fly data augmentation for SNN training.<n>An SNN trained on the generated I2E-ImageNet dataset achieves a state-of-the-art accuracy of 60.50%.
• Score: 5.758857776572054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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's effectiveness is demonstrated on large-scale benchmarks. An SNN trained on the generated I2E-ImageNet dataset achieves a state-of-the-art accuracy of 60.50%. Critically, this work establishes a powerful sim-to-real paradigm where pre-training on synthetic I2E data and fine-tuning on the real-world CIFAR10-DVS dataset yields an unprecedented accuracy of 92.5%. This result validates that synthetic event data can serve as a high-fidelity proxy for real sensor data, bridging a long-standing gap in neuromorphic engineering. By providing a scalable solution to the data problem, I2E offers a foundational toolkit for developing high-performance neuromorphic systems. The open-source algorithm and all generated datasets are provided to accelerate research in the field.

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