Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural Networks
This addresses the problem of slow SNN computations for researchers and practitioners in neuromorphic computing, offering a parallelization method that is incremental in improving efficiency.
The paper tackled the computational inefficiency of spiking neural networks (SNNs) due to sequential membrane potential updates by proposing Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method that achieves state-of-the-art accuracy and efficiency on neuromorphic datasets.
The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at~\url{https://github.com/chrazqee/MPE-PSN}. \end{abstract}