在神经形态芯片上实现低功耗强化学习，能效提升32倍。
Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform
- 用8比特量化细调脉冲网络，适配SpiNNaker2硬件
- 能效比GPU低32倍，推理延迟与GPU相当
- 适合实时机器人控制等低功耗场景
脉冲神经网络（SNNs）在神经形态硬件上可实现极低功耗和低延迟推理，适用于多种机器人任务。本文提出一种基于量化脉冲Q网络的强化学习算法，在两个经典控制任务中取得成效。网络先通过Q-learning训练，再经细调与8比特量化，部署于SpiNNaker2神经形态芯片。为评估其优势，我们对比了推理延迟、动态功耗及每次推理的能量成本，基准为GTX 1650 GPU。结果表明，SpiNNaker2在能效上最高降低32倍，推理延迟与GPU相当，部分任务下表现更优，验证了其在可扩展低能耗神经形态计算中的潜力，使神经形态深度Q学习成为高效强化学习的重要方向。
原文摘要 · Abstract (English)
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.
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