标题： 在SpiNNaker2神经形态平台上的脉冲Q网络的硬件感知微调 显示英文标题

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

摘要： 脉冲神经网络（SNNs）在针对各种机器人任务的神经形态硬件上，有望实现数量级更低的功耗和低延迟推理。 在本工作中，我们提出了一种基于量化SNNs的强化学习（RL）算法的节能实现，以解决两个经典控制任务。 该网络使用Q学习算法进行训练，然后进行微调并量化为低比特（8比特）精度，以便在SpiNNaker2神经形态芯片上进行嵌入式部署。 为了评估SpiNNaker2相对于传统计算平台的比较优势，我们分析了我们的SNN模型的推理延迟、动态功耗和每次推理的能量成本，并将其性能与GTX 1650 GPU基准进行比较。 我们的结果表明，SpiNNaker2在可扩展、低能耗的神经形态计算方面具有强大的潜力，实现了高达32倍的能量消耗减少。 推理延迟与基于GPU的执行相当，在某些任务设置中观察到了改进，这加强了SpiNNaker2在实时神经形态控制中的可行性，并使神经形态方法成为高效深度Q学习的有吸引力的方向。 显示英文摘要

摘要： 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.