Accelerated Deep-Learning inference on FPGAs in the Space Domain

Michael Petry, Patrick Gest, Andreas Koch, Max Ghiglione, Martin Werner

Abstract: Artificial intelligence has found its way into space, and similar to the situation on ground demands powerful hardware to unfold its full potential. With the heterogeneous compute platform that is offered by the space-grade variant of the Versal, AMD Xilinx presents a system that is particularly targeted at accelerating AI inference in space. This paper investigates the design flow and the achievable performance of this novel device. We present benchmark results in terms of concrete figures and measurements, i.e., throughput, latency, and power consumption, achieved by a predesigned hardware accelerator realized on the system, and compare them to a previous generation platform.