Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform

TL;DR

This paper demonstrates an energy-efficient implementation of quantized spiking neural networks for reinforcement learning on the SpiNNaker2 neuromorphic platform, achieving significant energy savings while maintaining real-time inference performance.

Contribution

It introduces a hardware-aware fine-tuning method for quantized SNNs on SpiNNaker2, optimizing energy efficiency for reinforcement learning tasks.

Findings

Up to 32x reduction in energy consumption compared to GPU

Inference latency comparable to GPU-based execution

Effective reinforcement learning on neuromorphic hardware

Abstract

Spiking Neural Networks (SNNs) promise orders-of-magnitude lower power consumption and low-latency inference on neuromorphic hardware for a wide range of robotic tasks. In this work, we present an energy-efficient implementation of a reinforcement learning (RL) algorithm using quantized SNNs to solve two classical control tasks. The network is trained using the Q-learning algorithm, then fine-tuned and quantized to low-bit (8-bit) precision for embedded deployment on the SpiNNaker2 neuromorphic chip. To evaluate the comparative advantage of SpiNNaker2 over conventional computing platforms, we analyze inference latency, dynamic power consumption, and energy cost per inference for our SNN models, comparing performance against a GTX 1650 GPU baseline. Our results demonstrate SpiNNaker2's strong potential for scalable, low-energy neuromorphic computing, achieving up to 32x reduction in energy consumption. Inference latency remains on par with GPU-based execution, with improvements observed in certain task settings, reinforcing SpiNNaker2's viability for real-time neuromorphic control and making the neuromorphic approach a compelling direction for efficient deep Q-learning.