Navigating Airworthiness Concerns with Deploying AI/ML Applications – A Brief Survey

Along with unique and challenging development concerns, target hardware deployment concerns exist for artificial intelligence (AI) and machine learning (ML) applications. Those deployment concerns should be addressed in the planning phase and consist of the issues surrounding the target hardware selection and the certifiability/qualifiable of the target hardware for the AI/ML model deployment. These concerns center around certification issues identified for multi-core processors (MCP), where those MCP issues are amplified for graphics processor units (GPUs) when they are used for general computing. While the use of complex graphics processors for general computing is being reconciled for flight critical applications, the reduction of these concerns is possible through design specific target hardware choices, e.g., selection of Field Programmable Gate Array (FPGA) devices or other certifiable approaches. This paper explores these concerns and proposes design specific target hardware choice strategies to mitigate those concerns.