Radiation Test and in Orbit Performance of MpSoC AI Accelerator

Abstract

Φ-Sat-1 is part of the European Space Agency initiative to promote the development of disruptive innovative technology and capabilities on-board EO missions. The Φ-Sat-l satellite represents the first-ever on-board Artificial Intelligence (AI) deep Convolutional Neural Network (CNN) inference on a dedicated chip attempting to exploit artificial Deep Neural Network (DNN) capability for Earth Observation. It utilises the Myriad Vision Processing Unit (VPU), a System On Chip (SOC) that has been designed ex novo for high-performance edge compute for vision applications. In order to support Myriad's deployment on Φ-Sat-l, the first mission using AI processing for operational purposes, and future applications in general, the SOC has undergone radiation characterisation via several test campaigns in European test facilities. The first AI application developed for in-flight inference was CloudScout, a segmentation neural network that was designed specifically for Φ-Sat-l in order to achieve high detail and good granularity in the classification result, and eventually discard on-board the cloudy images acquired by the hyperspectral sensor, thus greatly enhancing the data throughput capability of the mission. In addition to the CloudScout cloud detection AI SW results acquired during Φ-Sat-l's mission, in-flight performance data was also acquired for the hardware inference engine. Four separate VPU-based inference engine test phases were executed over 70 days during the mission. The in-flight diagnostics tests for the VPU inference engine indicate that the device performed as expected on-board Φ-Sat-l without experiencing any functional upsets, or any functional degradation effects due to radiation. All future installations of the Myriad VPU in space will be equipped with this Built-In Self Test (BIST) that will allow monitoring the performance of the inference engine hardware.