AMD's Instinct MI50 Used as Budget AI Testbed
Engineers are using older, cost-effective hardware for edge AI prototyping. A recent blog post details experimentation with AMD's Radeon Instinct MI50 (32GB) for AI development. While not an aerospace-certified part, it serves as an accessible platform for testing model performance and quantized inference in resource-constrained scenarios, offering a proxy for avionics hardware.
Originally a high-performance computing workhorse, the AMD Instinct MI50 launched in November 2018. Built on a 7nm process, it was aimed at datacenter and deep learning applications with a price tag that could reach thousands of dollars, around $4,850 for the 32GB model. Today, these cards can be found on second-hand markets for just a few hundred dollars. This positions the MI50 as an accessible testbed for prototyping avionics systems, which typically rely on more specialized, power-efficient, and radiation-hardened hardware. While GPUs are used, Field-Programmable Gate Arrays (FPGAs) are often favored in aerospace for their deterministic performance and lower latency, which are critical for safety and real-time response. FPGAs, however, demand a more intensive hardware engineering effort compared to the more common software-based development for GPUs. Prototyping on a consumer-grade GPU allows for rapid iteration before committing to the costly and complex process of developing for or selecting certified avionics hardware. The goal is not to fly the MI50, but to use it as a stand-in to evaluate how AI models will perform under constraints. This includes testing techniques like quantized inference, where model calculations are done with lower precision numbers (like 8-bit integers instead of 32-bit floating points) to save memory and power. This approach is crucial for developing software for systems that must eventually meet stringent safety standards like DO-178C. The use of AI and machine learning presents new challenges for this certification process, requiring extensive verification and validation to ensure reliability and predictability. Using inexpensive, older hardware to work out the kinks in model architecture and performance provides a significant cost and time advantage.
