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：随着边缘计算在智能家具、机器人及智能家居等领域的日益普及，本文提出了一种创新性控制方法HyperSNN，该方法融合了脉冲神经网络（SNNs）与超维计算。HyperSNN将昂贵的32位浮点数乘法运算替换为8位整数加法运算，在降低能耗的同时增强了鲁棒性，并可能提升模型精度。我们在AI Gym基准测试（包括Cartpole、Acrobot、MountainCar和Lunar Lander）上验证了该模型。实验表明，HyperSNN在实现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，我们的实验进一步证明HyperSNN具有更强的鲁棒性。我们认为HyperSNN特别适用于交互式设备、移动终端及可穿戴设备，能够推动能效优化与鲁棒系统设计。同时，该模型为模型预测控制（MPC）等复杂算法在真实工业场景中的实际部署开辟了新路径。