Autonomous embedded systems (e.g., robots) typically necessitate intelligent computation with low power/energy processing for completing their tasks. Such requirements can be fulfilled by embodied neuromorphic intelligence with spiking neural networks (SNNs) because of their high learning quality (e.g., accuracy) and sparse computation. Here, the employment of event-based data is preferred to ensure seamless connectivity between input and processing parts. However, state-of-the-art SNNs still face a long training time to achieve high accuracy, thereby incurring high energy consumption and producing a high rate of carbon emission. Toward this, we propose FastSpiker, a novel methodology that enables fast SNN training on event-based data through learning rate enhancements targeting autonomous embedded systems. In FastSpiker, we first investigate the impact of different learning rate policies and their values, then select the ones that quickly offer high accuracy. Afterward, we explore different settings for the selected learning rate policies to find the appropriate policies through a statistical-based decision. Experimental results show that our FastSpiker offers up to 10.5x faster training time and up to 88.39% lower carbon emission to achieve higher or comparable accuracy to the state-of-the-art on the event-based automotive dataset (i.e., NCARS). In this manner, our FastSpiker methodology paves the way for green and sustainable computing in realizing embodied neuromorphic intelligence for autonomous embedded systems.

翻译：自主嵌入式系统（如机器人）通常需要低功耗/低能耗的智能计算来完成其任务。这种需求可通过具身脉冲神经形态智能实现，因为脉冲神经网络（SNNs）具有高学习质量（如准确性）和稀疏计算特性。在此背景下，采用基于事件的数据有助于确保输入与处理部分的无缝连接。然而，最先进的SNNs仍面临训练时间长的问题，导致高能耗和高碳排放。为此，我们提出FastSpiker——一种面向自主嵌入式系统、通过增强学习率实现基于事件数据快速训练SNN的新方法。在FastSpiker中，我们首先研究不同学习率策略及其数值的影响，筛选出能快速实现高准确性的策略；随后通过基于统计的决策机制，探索选定学习率策略的不同配置以确定最优策略。实验结果表明，在基于事件的自动驾驶数据集（NCARS）上，FastSpiker相比现有技术最高可缩短10.5倍训练时间，降低88.39%碳排放，同时达到更高或相当的准确率。由此，FastSpiker方法为自主嵌入式系统实现具身神经形态智能开辟了绿色可持续计算的新路径。