Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Real-Time, Energy-Efficient, Sampling-Based Optimal Control via FPGA Acceleration

Tanmay Desai, Brian Plancher, R. Iris Bahar

TL;DR

This work presents a systematic FPGA co-design for Model Predictive Path Integral Control (MPPI) to run sampling-based stochastic optimal control on edge hardware. By decomposing MPPI into a four-stage, deeply pipelined dataflow—Gaussian noise generation, trajectory rollouts, cost calculations, and weighting/updates—the design exposes fine-grained parallelism and eliminates synchronization bottlenecks. Empirical results on a Xilinx ZCU102 show 2.33 ms per control step and 2.5×–5.4× energy reductions compared to embedded CPU/GPU baselines, with substantial improvements in end-to-end tracking accuracy and reliability. The work demonstrates that FPGA-based MPPI can achieve real-time, energy-efficient edge robotics performance and outlines a scalable blueprint for accelerating other sampling-based controllers.

Abstract

Autonomous mobile robots (AMRs), used for search-and-rescue and remote exploration, require fast and robust planning and control schemes. Sampling-based approaches for Model Predictive Control, especially approaches based on the Model Predictive Path Integral Control (MPPI) algorithm, have recently proven both to be highly effective for such applications and to map naturally to GPUs for hardware acceleration. However, both GPU and CPU implementations of such algorithms can struggle to meet tight energy and latency budgets on battery-constrained AMR platforms that leverage embedded compute. To address this issue, we present an FPGA-optimized MPPI design that exposes fine-grained parallelism and eliminates synchronization bottlenecks via deep pipelining and parallelism across algorithmic stages. This results in an average 3.1x to 7.5x speedup over optimized implementations on an embedded GPU and CPU, respectively, while simultaneously achieving a 2.5x to 5.4x reduction in energy usage. These results demonstrate that FPGA architectures are a promising direction for energy-efficient and high-performance edge robotics.

Real-Time, Energy-Efficient, Sampling-Based Optimal Control via FPGA Acceleration

TL;DR

This work presents a systematic FPGA co-design for Model Predictive Path Integral Control (MPPI) to run sampling-based stochastic optimal control on edge hardware. By decomposing MPPI into a four-stage, deeply pipelined dataflow—Gaussian noise generation, trajectory rollouts, cost calculations, and weighting/updates—the design exposes fine-grained parallelism and eliminates synchronization bottlenecks. Empirical results on a Xilinx ZCU102 show 2.33 ms per control step and 2.5×–5.4× energy reductions compared to embedded CPU/GPU baselines, with substantial improvements in end-to-end tracking accuracy and reliability. The work demonstrates that FPGA-based MPPI can achieve real-time, energy-efficient edge robotics performance and outlines a scalable blueprint for accelerating other sampling-based controllers.

Abstract

Autonomous mobile robots (AMRs), used for search-and-rescue and remote exploration, require fast and robust planning and control schemes. Sampling-based approaches for Model Predictive Control, especially approaches based on the Model Predictive Path Integral Control (MPPI) algorithm, have recently proven both to be highly effective for such applications and to map naturally to GPUs for hardware acceleration. However, both GPU and CPU implementations of such algorithms can struggle to meet tight energy and latency budgets on battery-constrained AMR platforms that leverage embedded compute. To address this issue, we present an FPGA-optimized MPPI design that exposes fine-grained parallelism and eliminates synchronization bottlenecks via deep pipelining and parallelism across algorithmic stages. This results in an average 3.1x to 7.5x speedup over optimized implementations on an embedded GPU and CPU, respectively, while simultaneously achieving a 2.5x to 5.4x reduction in energy usage. These results demonstrate that FPGA architectures are a promising direction for energy-efficient and high-performance edge robotics.
Paper Structure (16 sections, 6 equations, 8 figures, 2 tables, 1 algorithm)

This paper contains 16 sections, 6 equations, 8 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. MPPI Computational Dataflow Analysis
  5. MPPI FPGA Co-Design
  6. Overall Design
  7. Stage 1: Gaussian Noise Generation
  8. Stage 2: Trajectory Rollouts
  9. Stage 3: Cost Calculations
  10. Stage 4: Weighting and Control Sequence Updates
  11. Experiments and Result
  12. Methodology
  13. Latency Reduction
  14. Energy Reduction
  15. End-to-End Tracking Performance
  16. ...and 1 more sections

Figures (8)

  • Figure 1: This figure presents the overall FPGA architecture for the MPPI algorithm. a) illustrates the agent's rollouts, showing the states propagated over the prediction horizon for a single control time step. b) depicts the 4 key stages of the MPPI control update mapped to a dataflow architecture, highlighting the parallel and overlapping execution of these stages within one iteration. c) details the internal functionalities of each stage from (b), further decomposed into deep pipelined sub-stages to minimize latency and maximize throughput.
  • Figure 2: Array partitioning: Noise values for control arrays are partitioned into 4 sub-blocks. As shown, there are $K$ trajectories, where each trajectory produces $N$ pairs of noise values (for acceleration and steering) for the $N$ time steps, which are written to BRAM in blocks of 4 at a time.
  • Figure 3: Track 1
  • Figure 4: Track 2
  • Figure 5: Track 3
  • ...and 3 more figures