Autonomous mobile agents require low-power/energy-efficient machine learning (ML) algorithms to complete their ML-based tasks while adapting to diverse environments, as mobile agents are usually powered by batteries. These requirements can be fulfilled by Spiking Neural Networks (SNNs) as they offer low power/energy processing due to their sparse computations and efficient online learning with bio-inspired learning mechanisms for adapting to different environments. Recent works studied that the energy consumption of SNNs can be optimized by reducing the computation time of each neuron for processing a sequence of spikes (timestep). However, state-of-the-art techniques rely on intensive design searches to determine fixed timestep settings for only inference, thereby hindering SNNs from achieving further energy efficiency gains in both training and inference. These techniques also restrict SNNs from performing efficient online learning at run time. Toward this, we propose TopSpark, a novel methodology that leverages adaptive timestep reduction to enable energy-efficient SNN processing in both training and inference, while keeping its accuracy close to the accuracy of SNNs without timestep reduction. The ideas of TopSpark include analyzing the impact of different timesteps on the accuracy; identifying neuron parameters that have a significant impact on accuracy in different timesteps; employing parameter enhancements that make SNNs effectively perform learning and inference using less spiking activity; and developing a strategy to trade-off accuracy, latency, and energy to meet the design requirements. The results show that, TopSpark saves the SNN latency by 3.9x as well as energy consumption by 3.5x for training and 3.3x for inference on average, across different network sizes, learning rules, and workloads, while maintaining the accuracy within 2% of SNNs without timestep reduction.

翻译：自主移动平台通常依赖电池供电，因此需要低功耗/高能效的机器学习算法来完成基于机器学习任务，同时适应不同环境。脉冲神经网络因其稀疏计算实现低功耗/低能耗处理，并通过生物启发学习机制实现高效在线学习以适应不同环境，能够满足上述需求。近期研究表明，通过减少每个神经元处理脉冲序列的计算时间（时间步长），可优化脉冲神经网络的能耗。然而，现有技术依赖密集的设计空间搜索来确定仅用于推理的固定时间步长设置，这阻碍了脉冲神经网络在训练和推理中进一步提升能效。这些技术还限制了脉冲神经网络在运行时执行高效的在线学习。为此，我们提出TopSpark——一种通过自适应时间步长缩减实现训练与推理全流程高能效脉冲神经网络处理的新方法，同时保持其精度接近未缩减时间步长的脉冲神经网络。TopSpark的核心思路包括：分析不同时间步长对精度的影响；识别不同时间步长下对精度影响显著的神经元参数；采用参数增强策略使脉冲神经网络能通过更少的脉冲活动有效完成学习与推理；开发权衡精度、延迟与能耗的策略以满足设计需求。实验结果表明，在不同网络规模、学习规则和工作负载下，TopSpark平均将脉冲神经网络延迟降低3.9倍，训练能耗降低3.5倍，推理能耗降低3.3倍，同时将精度控制在未缩减时间步长的脉冲神经网络精度的2%以内。