Abstract
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.
Abstract (translated)
近年来，自动驾驶车辆（AGVs）、无人机（UAVs）和移动机器人等自主 agent有效提高了人类在解决多样化任务中的生产力。然而，由于这些 agent通常由便携式电池供电，因此它们在长时间内运行时需要极其低功耗/能量。为解决这个问题，神经形态计算作为一种有前景的解决方案应运而生，其中仿生 Spiking Neural Networks (SNNs) 使用基于事件的数据转换预处理或事件相机中的尖峰来执行稀疏计算 efficiently。然而，针对自主 agent 的 SNN 部署的研究仍处于早期阶段。因此，尚未对 enabling efficient embodied SNN deployments for autonomous agents 的优化阶段进行系统地定义。为了实现这一目标，我们提出了一个名为 SNN4Agents 的 novel framework，它包括一个针对自主 agent 应用设计能量高效的 embodied SNN 的优化技术集合。我们的 SNN4Agents 使用权重量化、时钟步减少和注意力窗口减少来共同提高能源效率、降低内存足迹、优化处理延迟，同时保持高精度。在评估中，我们研究了基于事件的汽车识别用例，并探讨了准确性、延迟、内存和能量消耗之间的权衡。实验结果表明，与最先进的 NCARS 数据集相比，我们的框架可以在降低 68.75% 的内存和使用 68.75% 更快的速度和 4.03x 的能量效率提升的同时保持高精度（即 84.12% 的准确率），从而实现自主 agent 的高效 embodied SNN 部署。
URL
https://arxiv.org/abs/2404.09331
