End-to-End Signal Processing and Deep Learning Using Embedded GPUs
The following presentation was given at NVIDIA's GPU Technology Conference (GTC) in Washington, DC on November 5, 2019. It was a great event where technology was showcased from many different research areas.
Presenter
Daniel Bryant
Abstract
We’ll present the GPU-accelerated digital signal processing (DSP) applications enabled by Deepwave Digital’s AI Radio Transceiver (AIR-T). We’ll also discuss our open source development tools and performance benchmarks for this new type of software-defined radio (SDR). By coupling NVIDIA’s TensorRT toolkit with the AIR-T, clients can rapidly develop and deploy deep learning applications at the edge of wireless systems. We’ll walk through a workflow for deep learning in wireless applications, including the acquisition of training data with the AIR-T, model training and optimization, and live inference on the AIR-T. Our solution addresses the issue of SDR bottlenecks. Because many DSP algorithms are highly parallelizable, GPUs can increase the throughput while maintaining simplistic programmability. With the new shared memory architecture of the NVIDIA Jetson products, GPUs are now a viable solution for optimizing short development times and high data rates while minimizing latency.
Presentation (pdf download)
