Embedded GPU Cluster Computing Framework for Inference of Convolutional Neural Networks

Abstract

The growing need for on-board image processing for space vehicles requires computing solutions that are both low-power and high-performance. Parallel computation using low-power embedded Graphics Processing Units (GPUs) satisfy both requirements. Our experiment involves the use of OpenMPI domain decomposition of an image processing algorithm based upon a pre-trained convolutional neural network (CNN) developed by the U.S. Air Force Research Laboratory (AFRL). Our testbed consists of six NVIDIA Jetson TX2 development boards operating in parallel. This parallel framework results in a speedup of $4.3 \times $ on six processing nodes. This approach also leads to a linear decay in parallel efficiency as more processing nodes are added to the network. By replicating the data across processors in addition to distributing, we also characterize the best-case impact of adding triple modular redundancy (TMR) to our application.