Speaker
Description
The historic success of the recent ESA Φ-sat-1 mission has demonstrated for the first time that COTS hardware acceleration of AI inference on a satellite payload in-orbit is now possible. The Deep Learning cloud detection solution deployed on Φ-sat-1 utilises an Intel Movidius Myriad 2 vision processor for inference compute. The Myriad has performance-per-watt and radiation characteristics that make it ideally suited as a payload data processor for satellite deployments, providing state-of-the-art Neural Network (NN) compute within an industry-low power envelope. Building on the hardware and software deployed on Φ-sat-1, the UB0100 CubeSat board is the next generation AI inference and Computer Vision (CV) engine that addresses the form factor and interface needs of CubeSats while exposing the compute of Myriad to the payload developer. This presentation discusses the requirements of an AI CubeSat payload data processing board (hardware, firmware, software), and demonstrates how the UB0100 solution addresses these requirements through its custom CubeSat build. An overview of the CVAI software that runs on the UB0100 will show how, in addition to AI inference and integration with popular AI frameworks, the user now has direct access to the hardware-accelerated vision functionality of the Myriad VPU. This unlocks combined image pre-processing and AI compute on a single device, enabling direct processing of data products at different levels on-satellite. The flexibility provided to the user by the UB0100 solution will be demonstrated through a selection of use cases.
