ML for Maintaining Obsolete Digital Systems

Overview

Computing systems in high-reliability deployments are often qualified for those deployments at system-level granularity while failures often occur at subsystem-level. Obsolescence poses a high risk to systems of this manner, as failing components often necessitate a complete system redesign or at least re-qualification, which is itself expensive and time-consuming. This project examines whether LLMs can be used to ease this burden by engineering drop-in replacements for failing, obsolete digital components based on the behavior of the original component and the broader design specifications of the system.

My Contributions

• Implemented an agent orchestration system designed to support LLM-based agents in writing RTL for obsolete digital components with hierarchical structure to improve legibility for human engineers.
• Designed and implemented a Retrieval Augmented Generation (RAG) system to provide additional context based on existing system documentation and the functional behavior of the obsolete digital component.