A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications

However, their incompatibility with traditi onal datasets, which consist of batches of input vectors rather t han spike trains, necessitates the development of efficient enc oding methods. This paper introduces a novel, open-source PyT orc h-compatible Python framework for spike encoding, designed f or neuromorphic applications in machine learning and reinfor cement learning. The framework supports a range of encoding algorithms, including Leaky Integrate-and-Fire (LIF), St ep Forward (SF), Pulse Width Modulation (PWM), and Ben's Spiker Algorithm (BSA), as well as specialized encoding strategie s covering population coding and reinforcement learning sce narios. Furthermore, we investigate the performance trade-offs of each method on embedded hardware using C/C++ implementations, considering energy consumption, computation time, spike s par-sity, and reconstruction accuracy. Our findings indicate th at SF typically achieves the lowest reconstruction error and off ers the highest energy efficiency and fastest encoding speed, achie ving the second-best spike sparsity. At the same time, other meth - ods demonstrate particular strengths depending on the sign al characteristics. This framework and the accompanying empi rical analysis provide valuable resources for selecting optimal encoding strategies for energy-efficient SNN applications.