In light of the increasing adoption of edge computing in areas such as intelligent furniture, robotics, and smart homes, this paper introduces HyperSNN, an innovative method for control tasks that uses spiking neural networks (SNNs) in combination with hyperdimensional computing. HyperSNN substitutes expensive 32-bit floating point multiplications with 8-bit integer additions, resulting in reduced energy consumption while enhancing robustness and potentially improving accuracy. Our model was tested on AI Gym benchmarks, including Cartpole, Acrobot, MountainCar, and Lunar Lander. HyperSNN achieves control accuracies that are on par with conventional machine learning methods but with only 1.36% to 9.96% of the energy expenditure. Furthermore, our experiments showed increased robustness when using HyperSNN. We believe that HyperSNN is especially suitable for interactive, mobile, and wearable devices, promoting energy-efficient and robust system design. Furthermore, it paves the way for the practical implementation of complex algorithms like model predictive control (MPC) in real-world industrial scenarios.

翻译：鉴于边缘计算在智能家具、机器人和智能家居等领域的日益普及，本文提出HyperSNN，一种结合脉冲神经网络与超维度计算的创新控制任务方法。HyperSNN用8位整数加法替代昂贵的32位浮点乘法，在降低能耗的同时增强了稳健性，并可能提升精度。我们在AI Gym基准测试（包括Cartpole、Acrobot、MountainCar和Lunar Lander）上对模型进行了验证。结果表明，HyperSNN在达到与传统机器学习方法相当的控制精度的同时，能耗仅为后者的1.36%至9.96%。此外，实验证明HyperSNN具有更强的稳健性。我们认为，HyperSNN特别适用于交互式、移动和可穿戴设备，可促进节能且稳健的系统设计，并为模型预测控制等复杂算法在实际工业场景中的落地应用铺平道路。