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：机器人学或其他尺寸、重量和功耗受限的边缘自主系统的应用，常需要实时且低能耗地解决大规模优化问题。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。本文提出一种方法，可在英特尔可扩展神经形态研究芯片Loihi 2上求解具有二次代价函数和线性约束的凸连续优化问题。当应用于四足机器人平台ANYmal的模型预测控制（MPC）问题时，该方法相比（边缘）CPU和GPU上的最先进求解器OSQP，在联合能量-延迟积上实现了超过两个数量级的降低，且针对不同问题规模，求解时间均在十毫秒以下。这些结果展示了非冯·诺依曼架构在机器人控制应用中的优势。