Deepwave Featured as Top 5 Things to See at NVIDIA’s GTC DC

The Premier AI Event is back in DC

"The center of the AI ecosystem shifts to D.C. this fall, when the GPU Technology Conference arrives at the Reagan Center in Washington, from Nov. 4-6."
Check out the full NVIDIA Blog post here.

The GTC DC event is a top-tier AI conference directed towards government, defense, and the private sector. Some of the best AI technology will be on display. Make sure to stop by and say hello to us.

Deepwave Digital will be at GTC DC on November5. Come see our presentation "End-to-end Signal Processing and Deep Learning Using Embedded GPUs" to learn what we have been up to. We will be showing you how to rapidly accelerate signal processing using embedded GPUs.

Custom GPU Signal Processing with GNU Radio on the AIR-T


This tutorial will teach you how to integrate GPU processing using CUDA with GNU Radio on the AIR-T software defined radio. Once completed, you will be able to build a custom GPU processing block within GNU Radio and use it to process your own signals. The tutorial assumes some familiarity in programming in Python and writing GPU kernels using CUDA. Don't worry if you have never written a GNU Radio block before, this part is explained for you and you can start by modifying the code in the tutorial's GitHub repository to get a feel for how all the components fit together.

The below sections will walk you through how to create a very simple GNU Radio block in Python that executes on the GPU. This simple block will use the PyCUDA library to divide the input signal by two and send the result to the output. All computation within the block will be done on the GPU. It is meant to be a very simple framework to understand the process of developing signal processing code.

Note that this tutorial documents software APIs that may change over time. In all of the below examples, we will be working with GNURadio 3.7.9 (as released with Ubuntu 16.04 LTS) and developing blocks using Python 2.7.

Click here for the full tutorial

cuFFT on the AIR-T with GNU Radio

FFTs with CUDA on the AIR-T with GNU Radio

GPUs are extremely well suited for processes that are highly parallel. The Fast Fourier Transform (FFT) is one of the most common techniques in signal processing and happens to be a highly parallel algorithm. In this blog post the Deepwave team walks you though how to leverage the embedded GPU built into the AIR-T to perform high-speed FFTs without the computational bottleneck of a CPU and without having to experience the long development cycle associated with writing VHDL code for FPGAs. By leveraging the GPU on the AIR-T, you get the best of both worlds: fast development time and high speed processing.

You may not be aware, but a while back we pushed a new block to our open source GR-Wavelearner software: a processing block that allows customers to leverage NVIDIA's extremely efficient cuFFT algorithm on the AIR-T, out of the box. Because the AIR-T is the only Software Defined Radio (SDR) with native GPU support, it may be leveraged to accelerate FFT processing capability with very little programming expertise. Here is the short, three step process.

Click here for the full tutorial

If you do not yet own an AIR-T, please visit our webpage for more information or submit an inquiry to talk to our sales team.

New AIR-T Enclosures

AIR-T Enclosures Fresh off the Production Line

We have just received our first production versions of the new AIR-T software defined radio enclosure and it is beautiful. If you already have and AIR-T, you can order a kit today to protect your SDR. If you are thinking about acquiring our GPU enabled SDR, make sure to talk with us about the enclosure.

The enclosure is expertly constructed from aluminum to produce a polished, elegant, and sleek metallic silver finish. It measures 192 x 182 x 79 mm (7.5 x 7.2 3.1 inches) and the power button illuminates blue when they system is on. All RF ports are brought to the front of the enclosure for ease of use and all computer peripherals connections are brought to the rear.

Submit a sales inquiry here

AIR-T Enclosure Front

AIR-T Enclosure Back