2023/08/05 by Yuhui Hao, Hao, Yuhui, Bo Yu +5
Computer Science · #Error Correcting Code Techniques #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Neural Networks and Applications #Parallel Computing and Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2308.02768
openalex publication_date 2023/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Factor graph represents the factorization of a probability distribution function and serves as an effective abstraction in various autonomous machine computing tasks. Control is one of the core applications in autonomous machine computing stacks. Among all control algorithms, Linear Quadratic Regulator (LQR) offers one of the best trade-offs between efficiency and accuracy. However, due to the inherent iterative process and extensive computation, it is a challenging task for the autonomous systems with real-time limits and energy constrained. In this paper, we present FGLQR, an accelerator of LQR control for autonomous machines using the abstraction of a factor graph. By transforming the dynamic equation constraints into least squares constraints, the factor graph solving process is more hardware friendly and accelerated with almost no loss in accuracy. With a domain specific parallel solving pattern, FGLQR achieves 10.2x speed up and 32.9x energy reduction compared to the software implementation on an advanced Intel CPU.