MPC solver hardware generation framework with model-specific operation fusion and pruning
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Model Predictive Control (MPC) is a state-of-the-art and robust control framework. However, its stringent performance requirements for computational infrastructure, and its need for real-time computation at the edge, hinder its widespread adoption in various application scenarios. This is particularly challenging for the MCUs commonly found on tiny robots. To address this computational challenge, we developed a flexible MPC solver hardware generation framework which includes a parameterized and programmable vector architecture template that accommodates instruction-level and data-level parallelism in vector and matrix functional units, and a model-specific fused architecture. Implementation of the proposed processor on the Ultra96 platform achieves up to a 9.73x speedup compared to existing generic solutions on MCUs. Moreover, end-to-end performance tests reveal that this speedup reduces the overall control error by 25.96%. Overall, the enhanced flexibility and performance of our proposed processor design open up the potential for MPC to be utilized in a broader range of applications.
Date Acceptance
2025-10-20
Citation
2025 International Conference on Field Programmable Technology (ICFPT)
Publisher
IEEE
Journal / Book Title
2025 International Conference on Field Programmable Technology (ICFPT)
Copyright Statement
Copyright This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
Source
2025 International Conference on Field Programmable Technology (ICFPT)
Publication Status
Accepted
Start Date
2025-12-02
Finish Date
2025-12-05
Coverage Spatial
Shanghai, China
Date Publish Online
2026-02-03