Memory-Free and Parallel Computation for Quantized Spiking Neural Networks
This work addresses energy efficiency and deployment on resource-limited edge devices for neuromorphic computing, though it appears incremental as it builds on existing QSNN methods.
The paper tackles the performance decline in Quantized Spiking Neural Networks (QSNNs) due to quantized membrane potential by introducing a memory-free quantization method and a parallel computation framework, resulting in state-of-the-art performance on image datasets with less memory and faster training speeds.
Quantized Spiking Neural Networks (QSNNs) offer superior energy efficiency and are well-suited for deployment on resource-limited edge devices. However, limited bit-width weight and membrane potential result in a notable performance decline. In this study, we first identify a new underlying cause for this decline: the loss of historical information due to the quantized membrane potential. To tackle this issue, we introduce a memory-free quantization method that captures all historical information without directly storing membrane potentials, resulting in better performance with less memory requirements. To further improve the computational efficiency, we propose a parallel training and asynchronous inference framework that greatly increases training speed and energy efficiency. We combine the proposed memory-free quantization and parallel computation methods to develop a high-performance and efficient QSNN, named MFP-QSNN. Extensive experiments show that our MFP-QSNN achieves state-of-the-art performance on various static and neuromorphic image datasets, requiring less memory and faster training speeds. The efficiency and efficacy of the MFP-QSNN highlight its potential for energy-efficient neuromorphic computing.