Qualcomm RB5 AI Hardware Acceleration with TensorFlow

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Qualcomm Robotics RB5/RB6
Introduction
SoM Overview Why the RB5/RB6? Carrier Boards Qualcomm Robotics RB5 developer kit Qualcomm Robotics RB6 platform
Software Development Support
Boot Devices Thundercomm SDK Downloading SDK Installing SDK Generating Image and flashing Linaro Getting Started Flashing Bootloader Flashing Boot Image and Rootfs Special / Debug Modes Setting up board in EDL Mode Setting up board in Fastboot Mode
Development in the Board
Getting into the Board Using ADB Using Serial Console Using SSH Device Setup WiFi setup HDMI setup Update Ubuntu Packages
Build from source
Base Image Customize Image
Capture Subsystem
Hardware Capture Components Software Capture Components Building your own camera sensor driver
Image Processing and Acceleration Software
GPU Profiling Fast CV Installing Hexagon SDK Installing Fast CV Building your own Fast CV application
GStreamer Pipelines
Capture and Display Video Encoding Video Decoding
AI Acceleration
Neural Processing SDK Downloading Requirements Setup SDK Environment Install qtimlesnpe Running Inception v3 Running MobileNetV2 + SSD TensorFlow Example pipeline TensorFlow to DLC Conversion
Sensors
IMU
Demos
Drone Demo Smart Camera
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In the section AI Acceleration we had an overview of our options to work Machine Learning/AI applications in the Qualcomm Robotics RB5/RB6. In this section, we are seeing the TensorFlow option. This option comes from an Android specific API that has been ported to the Qualcomm Robotics RB5/RB6 board. We are going to see an example on how to use the GStreamer plugin for TFLite's models and the performance it has in the board.

• The Example pipeline section shows a functional GStreamer pipeline that used a TFLite model in the Qualcomm Robotics RB5/RB6.

• The TensorFlow to DLC Conversion section shows how to use tools for converting machine learning models into a format optimized for execution on devices powered by Qualcomm Snapdragon processors.