A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications
This work addresses the incompatibility issue for researchers and practitioners in neuromorphic computing, offering an incremental improvement through a new framework and empirical analysis.
The paper tackles the problem of converting traditional datasets into spike trains for Spiking Neural Networks (SNNs) by introducing a PyTorch-compatible spike encoding framework, with results showing that the Step Forward (SF) method achieves the lowest reconstruction error, highest energy efficiency, and fastest encoding speed, while other methods have specific strengths.
Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. However, their incompatibility with traditional datasets, which consist of batches of input vectors rather than spike trains, necessitates the development of efficient encoding methods. This paper introduces a novel, open-source PyTorch-compatible Python framework for spike encoding, designed for neuromorphic applications in machine learning and reinforcement learning. The framework supports a range of encoding algorithms, including Leaky Integrate-and-Fire (LIF), Step Forward (SF), Pulse Width Modulation (PWM), and Ben's Spiker Algorithm (BSA), as well as specialized encoding strategies covering population coding and reinforcement learning scenarios. Furthermore, we investigate the performance trade-offs of each method on embedded hardware using C/C++ implementations, considering energy consumption, computation time, spike sparsity, and reconstruction accuracy. Our findings indicate that SF typically achieves the lowest reconstruction error and offers the highest energy efficiency and fastest encoding speed, achieving the second-best spike sparsity. At the same time, other methods demonstrate particular strengths depending on the signal characteristics. This framework and the accompanying empirical analysis provide valuable resources for selecting optimal encoding strategies for energy-efficient SNN applications.