Speaker
Description
Constructing a framework for the development of SDR applications backed by heterogenous compute platforms has been the subject of significant development effort over a few decades, but no single framework has gained significant traction in the open-source SDR community. At the same time, machine-learning software has matured and made efficiently implementing tensor operations across CPU, GPU, and FPGA accessible even to novice developers. We present a novel GNU Radio out-of-tree (OOT) module that integrates with Triton Inference Server (TIS) -- effectively enabling highly-scalable SDR application development backed by heterogenous compute platforms. The new OOT brings exciting capabilities to the GR 3.x line of development which is currently available, but also presents the first significant use of the GR 4.x modular scheduler framework. Finally, we present a number of performance comparisons demonstrating the immediate benefit the new OOT brings to application developers.
| Talk Length | 15 Minutes |
|---|---|
| Acknowledge | Acknowledge In-Person |
