Commentary: Human Expertise in FPGA Design Remains Irreplaceable

- FPGAs offer significant advantages in aerospace for tasks requiring high-throughput, low-latency processing such as in radar and electronic warfare systems, due to their parallel architecture and deterministic nature. In contrast, GPUs, while powerful for floating-point operations, are less deterministic, which can be a critical drawback in safety-critical flight systems. - The stringent DO-254 standard for airborne electronic hardware is a major reason why human expertise remains critical in aerospace FPGA development. This standard mandates a rigorous, traceable design assurance process, from requirements to verification, to ensure safety in complex systems where failures could be catastrophic. - While AI is being explored to assist in FPGA design, its application in safety-critical aerospace systems is limited by the "black box" nature of many AI models, which complicates the verification and traceability required by standards like DO-254. Human engineers are still required for final verification, sign-off, and to make high-level architectural decisions that balance trade-offs between performance, power, and safety. - Model-Based Systems Engineering (MBSE) is increasingly used to manage the complexity of aerospace systems, including those with FPGAs. MBSE uses digital models to define system requirements, design, and analysis, which helps reduce design errors early in the development lifecycle and provides a "single source of truth" for complex projects. - For AI and machine learning applications at the edge, FPGAs provide a power-efficient and reconfigurable hardware solution, which is crucial for resource-constrained environments like aircraft and satellites. Their ability to be updated in the field extends the operational life of critical aerospace and defense equipment. - The development of AI-powered tools like RapidGPT and Moku Cloud Compile with ChatGPT is aimed at accelerating FPGA development by assisting with HDL code generation and creating prototypes. However, these tools are currently positioned as assistants to engineers, not replacements, particularly for complex and critical design tasks. - A significant challenge for AI in FPGA design is the lack of large, high-quality, public datasets of FPGA designs for training models. Much of the necessary data is proprietary and specific to individual companies and projects, creating data silos that hinder the development of more advanced AI design tools. - In a military and aerospace context, a "triplicate architecture" combining a CPU, GPU, and FPGA is an emerging trend to leverage the strengths of each. This hybrid approach allows systems to use the most suitable processor for different tasks, such as using a GPU for complex AI algorithms and an FPGA for real-time, deterministic processing.