Accelerating Robot Dynamics Gradients on a CPU, GPU, and FPGA
Name
icra21.pdf
Description
Accepted version
Size
454.41 KB
Format
Adobe PDF
Checksum (MD5)
58d212dda143e5ffcb9d5e5c02dfecd4
Author(s)
Plancher, Brian
Neuman, Sabrina M
Bourgeat, Thomas
Kuindersma, Scott
Devadas, Srinivas
Reddi, Vijay Janapa
Date Issued
2021
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Plancher, Brian, Neuman, Sabrina M, Bourgeat, Thomas, Kuindersma, Scott, Devadas, Srinivas et al. 2021. "Accelerating Robot Dynamics Gradients on a CPU, GPU, and FPGA." IEEE Robotics and Automation Letters, 6 (2).
Version
Author's final manuscript
Abstract
Computing the gradient of rigid body dynamics is a central operation in many state-of-the-art planning and control algorithms in robotics. Parallel computing platforms such as GPUs and FPGAs can offer performance gains for algorithms with hardware-compatible computational structures. In this letter, we detail the designs of three faster than state-of-the-art implementations of the gradient of rigid body dynamics on a CPU, GPU, and FPGA. Our optimized FPGA and GPU implementations provide as much as a 3.0x end-to-end speedup over our optimized CPU implementation by refactoring the algorithm to exploit its computational features, e.g., parallelism at different granularities. We also find that the relative performance across hardware platforms depends on the number of parallel gradient evaluations required.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/LRA.2021.3057845
