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

2025-04-15

AI Summary

To address the incompatibility between conventional static datasets and spiking neural network (SNN) inputs, this work introduces the first fully PyTorch-compatible, modular, open-source spike encoding framework. The framework unifies support for diverse encoding strategies—including leaky integrate-and-fire (LIF), step-forward (SF), pulse-width modulation (PWM), and Ben’s Spiker Algorithm (BSA)—and presents the first systematic evaluation of their energy–accuracy–latency trade-offs on embedded hardware. Experimental results demonstrate that SF encoding achieves the best overall performance in reconstruction error, energy efficiency, and encoding speed, while maintaining near-optimal spike sparsity. The framework has been validated on real embedded platforms and provides a reproducible, empirically grounded guideline for encoding selection. By abstracting low-level hardware constraints and standardizing encoding interfaces within PyTorch, it significantly lowers the barrier to SNN deployment in resource-constrained environments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Evolutionary Computation
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Develops PyTorch-compatible spike encoding for neuromorphic energy efficiency
• Evaluates encoding methods' trade-offs in energy, speed, and accuracy
• Provides framework for optimal encoding in SNN machine learning applications

Innovation

• PyTorch-compatible spike encoding framework
• Supports multiple encoding algorithms
• Optimizes energy efficiency and performance