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Rapid Prototyping on NVIDIA Jetson Platforms with MATLAB

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This article was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA.

This article discusses how an application developer can prototype and deploy deep learning algorithms on hardware like the NVIDIA Jetson Nano Developer Kit with MATLAB. In previous posts, we explored how you can design and train deep learning networks in MATLAB and how you can generate optimized CUDA code from your deep learning algorithms.

In our experience working with deep learning engineers, we often see they run into challenges when prototyping with real hardware because they have to manually integrate their entire application code, such as interfaces with the sensors on the hardware, or integrate with the necessary toolchain to deploy and run the application on the hardware. If the algorithm does not have the expected behavior or if it does not meet the performance expectation, they have to go back to their workstation to debug the underlying cause.

This post shares how an application developer can deploy, validate and verify their MATLAB algorithms on real hardware like the NVIDIA Jetson platform by:

  • Using live data from the Jetson board to improve algorithm robustness
  • Using hardware-in-the-loop simulation for verification and performance profiling
  • Deploying standalone applications on the Jetson board

Using NVIDIA Jetson with MATLAB

MATLAB makes it easier to prototype and deploy to NVIDIA hardware through the NVIDIA hardware support package. It provides simple APIs for interactive workflow as well as standalone execution and enables you to:

  1. Connect directly to the hardware from MATLAB and test your application on sensor data from the hardware
  2. Deploy the standalone application to the Jetson board
  3. Debug any issues before deploying a standalone application to the Jetson board 

These workflows steps are illustrated in the figure below:

Figure 1: Illustrating the three steps of the workflow

The support package supports the NVIDIA Jetson TK1, Jetson TX1, Jetson TX2, Jetson Xavier and Jetson Nano developer kits. It also...