Soft Error Reliability Aware EDA
Description
This review explores advancements in GPU-accelerated EDA tools, such as DREAM Place and GPU-based Rectilinear Steiner Tree generation, which leverage AI and parallelism to optimize placement, routing, and logic synthesis tasks. These tools significantly reduce runtime and enhance placement accuracy, facilitating faster design iterations and improving fault tolerance by minimizing soft error hotspots. Additionally, AI-driven frameworks, including deep learning-based layout optimization, show promise in addressing pin density challenges, congestion, and manufacturability constraints. While these innovations enhance performance, gaps remain in ensuring radiation-hardened reliability for aerospace and military applications. Existing research primarily focuses on optimizing computational efficiency, but further investigation is needed into integrating fault-tolerant microcontroller design within AI-accelerated EDA workflows. Specifically, challenges in mitigating Single Event Upsets (SEUs) and enhancing real-time processing capabilities for mission-critical hardware require deeper exploration. Future work should focus on bridging the gap between AIdriven optimization and the development of robust, radiation-resistant microcontrollers to ensure reliable operation in extreme environments