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Toward High-Accuracy and Low-Latency Spiking Neural Networks With Two-Stage Optimization
IEEE Transactions on Neural Networks and Learning Systems
|December 15, 2023
Summary
This study introduces a novel two-stage algorithm for converting artificial neural networks (ANNs) to spiking neural networks (SNNs), significantly reducing accuracy loss for efficient, low-latency edge device applications.
Area of Science:
- Artificial Intelligence
- Computational Neuroscience
Background:
- Spiking neural networks (SNNs) offer energy efficiency through sparse computation.
- Artificial neural network (ANN)-SNN conversion is a popular method for deep SNN implementation.
- Existing conversion methods suffer from accuracy loss, especially with limited time steps, hindering edge device applications.
Purpose of the Study:
- To address the accuracy degradation in ANN-SNN conversion.
- To improve the performance of SNNs on latency-sensitive edge devices.
Main Methods:
- Identified performance degradation sources: misrepresentation of residual membrane potential.
- Decomposed conversion error into quantization, clipping, and residual potential representation errors.
- Proposed a two-stage conversion algorithm to minimize these errors.
Main Results:
- The two-stage algorithm significantly minimizes conversion errors.
- Achieved state-of-the-art performance in accuracy, latency, and energy efficiency on CIFAR-10, CIFAR-100, and ImageNet datasets.
- Demonstrated notable gains in object detection regression performance under ultra-low latency.
Conclusions:
- The proposed conversion method effectively enhances SNN accuracy and efficiency.
- This approach enables broader applications of SNNs in resource-constrained edge computing.
- The algorithm shows promise for real-time, low-power AI tasks.
