Applications in robotics or other size-, weight- and power-constrained autonomous systems at the edge often require real-time and low-energy solutions to large optimization problems. Event-based and memory-integrated neuromorphic architectures promise to solve such optimization problems with superior energy efficiency and performance compared to conventional von Neumann architectures. Here, we present a method to solve convex continuous optimization problems with quadratic cost functions and linear constraints on Intel's scalable neuromorphic research chip Loihi 2. When applied to model predictive control (MPC) problems for the quadruped robotic platform ANYmal, this method achieves over two orders of magnitude reduction in combined energy-delay product compared to the state-of-the-art solver, OSQP, on (edge) CPUs and GPUs with solution times under ten milliseconds for various problem sizes. These results demonstrate the benefit of non-von-Neumann architectures for robotic control applications.

翻译：在机器人学或边缘计算中其他受尺寸、重量和功耗约束的自主系统应用中，通常需要实时且低能耗地求解大规模优化问题。基于事件驱动和内存集成的神经形态架构有望以优于传统冯·诺依曼架构的能效和性能解决此类优化问题。本文提出一种在英特尔可扩展神经形态研究芯片Loihi 2上求解具有二次成本函数和线性约束的凸连续优化问题的方法。当应用于四足机器人平台ANYmal的模型预测控制（MPC）问题时，该方法相较于（边缘）CPU和GPU上的先进求解器OSQP，在多种问题规模下（求解时间低于10毫秒）实现了两个数量级以上的能量-延迟积综合降低。这些结果证明了非冯·诺依曼架构在机器人控制应用中的优势。