Speaker
Description
In the past decade, the field of Machine Learning has witnessed dramatic breakthroughs in the state of the art for tasks such as image classification and object detection, aided by advancements in algorithms, training data and computing architectures. To date, most results have been demonstrated for terrestrial applications, but there is significant demand for solutions that can scale these capabilities into the Space environment, where on-board Machine Learning combined with high performance sensor packages could offer a dramatic reduction in decision latency. In this session, we will discuss Xilinx hardware and software solutions suitable for enabling high performance, low-latency, SWaP-optimized machine learning acceleration for Space applications; as well as the associated design requirements. Solution types covered include the Xilinx Deep Learning Processor Unit, as well as third-party and fabric-based options.
