  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.
