Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically powered by portable batteries, they require extremely low power/energy consumption to operate in a long lifespan. To solve this challenge, neuromorphic computing has emerged as a promising solution, where bio-inspired Spiking Neural Networks (SNNs) use spikes from event-based cameras or data conversion pre-processing to perform sparse computations efficiently. However, the studies of SNN deployments for autonomous agents are still at an early stage. Hence, the optimization stages for enabling efficient embodied SNN deployments for autonomous agents have not been defined systematically. Toward this, we propose a novel framework called SNN4Agents that consists of a set of optimization techniques for designing energy-efficient embodied SNNs targeting autonomous agent applications. Our SNN4Agents employs weight quantization, timestep reduction, and attention window reduction to jointly improve the energy efficiency, reduce the memory footprint, optimize the processing latency, while maintaining high accuracy. In the evaluation, we investigate use cases of event-based car recognition, and explore the trade-offs among accuracy, latency, memory, and energy consumption. The experimental results show that our proposed framework can maintain high accuracy (i.e., 84.12% accuracy) with 68.75% memory saving, 3.58x speed-up, and 4.03x energy efficiency improvement as compared to the state-of-the-art work for NCARS dataset, thereby enabling energy-efficient embodied SNN deployments for autonomous agents.

翻译：近年趋势表明，自主地面车辆、无人机及移动机器人等自主智能体在解决多样化任务中有效提升了人类生产力。然而，由于这类智能体通常由便携电池供电，其需具备极低功耗/能耗以实现长时运行。针对这一挑战，神经形态计算作为极具前景的解决方案应运而生：受生物启发的脉冲神经网络通过事件相机或数据转换预处理产生的脉冲信号，高效执行稀疏计算。但目前面向自主智能体的SNN部署研究仍处早期阶段，尚未系统定义实现高效具身SNN部署的优化体系。为此，我们提出名为SNN4Agents的新型框架，该框架包含一套优化技术，旨在为自主智能体应用设计节能型具身脉冲神经网络。SNN4Agents采用权重量化、时间步缩减及注意力窗口缩减等联合优化策略，在维持高精度的同时，协同提升能效、降低内存占用、优化处理延迟。在评估环节，我们研究了基于事件相机的车辆识别用例，并探索了精度、延迟、内存与能耗间的权衡关系。实验结果表明，与NCARS数据集上的现有最优方法相比，所提框架可实现84.12%的高精度，同时节省68.75%内存，获得3.58倍加速和4.03倍能效提升，从而为自主智能体实现节能型具身SNN部署提供可行方案。