Uncrewed Systems Technology 049 - April/May 2023

6 April/May 2023 | Uncrewed Systems Technology Mission-critical info for UST professionals Platform one Intel and Daedalean have developed the first multi-core reference design for certifiable avionics using machine learning, or ML (writes Nick Flaherty). The design provides vision-based situational awareness using neural networks with high-resolution, high-throughput camera inputs based on Intel’s 11thGen Core i7 andAgilex F-Series FPGAs. On top of the SWaP constraints, autonomous aircraft developers face two other challenging circumstances when incorporating ML and AI into avionics systems. First, ML and neural network applications have increasingly high computational requirements. Second, no ML application has yet been certified by aviation regulators. The problem is that processor manufacturers typically withhold details about how a multi-core processor’s shared cache works, such as how cache lines are flushed, despite the common understanding that cache operation has a major impact on the determinism of how applications run. Intel has already introduced the Airworthiness Evidence Package (AEP) that provides manufacturers with processor artefacts and the analysis and mitigation of non-deterministic and unintended behaviour to support DO- 254 certification up to design assurance level (DAL) A for aircraft. Now Daedalean has designed a system for a sensor computer using the AEP and Intel processors for use in AI/ML aviation applications and with the available documentation to support certification. This is the first real-world working example to provide guidance on how to approach these challenges in general: how to ensure an ML-based system can Airborne vehicles Avionics design usesML Daedalean’s software provides certifiable machine learning in safety-critical aerospace applications