SpikeRL: A Scalable and Energy-efficient Framework for Deep Spiking Reinforcement Learning

2025-02-21

AI Summary

To address the bottlenecks of low performance, high energy consumption, and poor scalability in spiking neural network (SNN)-based deep reinforcement learning (DeepRL) for large-scale continuous control tasks, this work proposes the first distributed-training-enabled DeepRL-SNN framework. The framework innovatively integrates population-coded spiking actor networks, mixed-precision training, and fine-grained spiking neuron dynamics modeling, and achieves efficient parallelization via PyTorch Distributed with the NCCL backend. Compared to the state-of-the-art DeepRL-SNN approaches, our method achieves a 4.26× speedup in training throughput while maintaining comparable decision-making quality, and reduces energy consumption by 2.25×. These improvements significantly enhance the scalability, energy efficiency, and practical sustainability of SNNs in complex continuous control scenarios.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In this era of AI revolution, massive investments in large-scale data-driven AI systems demand high-performance computing, consuming tremendous energy and resources. This trend raises new challenges in optimizing sustainability without sacrificing scalability or performance. Among the energy-efficient alternatives of the traditional Von Neumann architecture, neuromorphic computing and its Spiking Neural Networks (SNNs) are a promising choice due to their inherent energy efficiency. However, in some real-world application scenarios such as complex continuous control tasks, SNNs often lack the performance optimizations that traditional artificial neural networks have. Researchers have addressed this by combining SNNs with Deep Reinforcement Learning (DeepRL), yet scalability remains unexplored. In this paper, we extend our previous work on SpikeRL, which is a scalable and energy efficient framework for DeepRL-based SNNs for continuous control. In our initial implementation of SpikeRL framework, we depended on the population encoding from the Population-coded Spiking Actor Network (PopSAN) method for our SNN model and implemented distributed training with Message Passing Interface (MPI) through mpi4py. Also, further optimizing our model training by using mixed-precision for parameter updates. In our new SpikeRL framework, we have implemented our own DeepRL-SNN component with population encoding, and distributed training with PyTorch Distributed package with NCCL backend while still optimizing with mixed precision training. Our new SpikeRL implementation is 4.26X faster and 2.25X more energy efficient than state-of-the-art DeepRL-SNN methods. Our proposed SpikeRL framework demonstrates a truly scalable and sustainable solution for complex continuous control tasks in real-world applications.

Problem

• Optimizing energy efficiency in AI systems
• Enhancing scalability of Spiking Neural Networks
• Improving performance in continuous control tasks

Innovation

• DeepRL-SNN with population encoding
• Distributed training via PyTorch NCCL
• Mixed precision for energy efficiency