# Neuromorphic quadratic programming for efficient and scalable model predictive control

arXiv: https://arxiv.org/abs/2401.14885 · HTML: https://mlsanity.com/paper/2401.14885/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2024.

## Built on by (most cited first, 6 of 6)

- [A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processing](https://mlsanity.com/paper/2409.15022/) (2024)
- [Delay-Aware Diffusion Policy: Bridging the Observation-Execution Gap in Dynamic Tasks](https://mlsanity.com/paper/2512.07697/) (2025)
- [Solving QUBO on the Loihi 2 Neuromorphic Processor](https://mlsanity.com/paper/2408.03076/) (2024)
- [Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms](https://mlsanity.com/paper/2505.16362/) (2025)
- [Event-driven Robust Fitting on Neuromorphic Hardware](https://mlsanity.com/paper/2508.09466/) (2025)
- [Robust PnP on a Neuromorphic Processor for Object Pose Estimation](https://mlsanity.com/paper/2607.16834/) (2026)

## Abstract (arXiv)

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.