GPU-Computer Cluster System to Enable Deep-Learning Research for Robotic Perception and Motion Planning Algorithms
Abstract
Learning from data is one of the key ways to increase the capability of the next generation ofautonomous robots. While the amount of datasets available to us is large (> 50TB), processingand utilizing this data with commonly available computing resources is not possible. Therefore,we request DURIP funds to purchase a GPU-computer cluster system to enable large-scale deeplearningresearch for robotic perception and motion planning algorithms. The proposed systemconsists of a head node with 88TB of storage, and 17 compute nodes with 4 GPUs and 2 CPUs each.The proposed cluster enables us to push the state of the art in perception and motion planning ofseveral existing ONR and other government sponsored projects. Examples of research areas thatwill be enhanced by the cluster include semantic classification, high-performance motion planning,large scale feature mapping, and image processing algorithms. The proposed system is a catalystthat will amplify our capability to process large datasets and perform large experiments leading tonew advances in autonomy research.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 30, 2016
- Source ID
- N000141612903
Entities
People
- Scott Sanders
Organizations
- Massachusetts Institute of Technology
- Office of Naval Research
- United States Navy