Summary
Artificial intelligence (AI) is becoming integral to manufacturing, healthcare, and autonomous systems, creating an urgent need for reliable deployment across diverse computing platforms. Yet dependable deployment remains difficult because the software systems that adapt learned models to target devices are complex and fragile. An AI model that appears valid at a high level can still fail during deployment because of hidden resource limits, data layout requirements, and platform-specific transformations. These failures are especially concerning because they may silently alter outputs rather th
