Presentation
Agentic AI Approach to Optimize Front-End EDA Tools Flow
DescriptionVerifying server-grade Hardware Description Languages (HDLs) is a complex task that requires sophisticated front-end Electronic Design Automation (EDA) tools. To optimize performance, these tools abstract the HDLs and utilize intermediate representations. During runtime, EDA tools may also append additional information to facilitate verification. However, this abstraction and annotation process can create a significant disconnect between the user's input and the tool's output, necessitating extensive manual intervention from a subject matter expert to debug tool's error.
Artificial Intelligence (AI) can help bridge this gap and accelerate error interpretation and debugging. Nevertheless, foundational AI models lack proprietary domain/design-specific information. To address this limitation, AI agents can be employed to leverage existing EDA tools and gather domain/design-specific information, thereby facilitating more accurate error interpretation and debugging.
This presentation will showcase the application of AI agents in IBM's static structural checking tool, which is intensively used to ensure HDL compliance with IBM's design methodology. The tool operates on abstracted HDLs represented as a graph of BOXES and NETS, utilizing ANTLR grammar to traverse the graph. During traversal, the tool appends additional labels and tags to facilitate checking. However, the resulting error traces are often cryptic and require expert interpretation, slowing down the debugging process. By integrating AI agents, we aim to bridge the gap between tool errors and user understanding of the HDL, thereby accelerating debugging efforts by at least 30%. This innovative approach has the potential to significantly improve the efficiency and productivity of HDL verification and debugging processes.
Artificial Intelligence (AI) can help bridge this gap and accelerate error interpretation and debugging. Nevertheless, foundational AI models lack proprietary domain/design-specific information. To address this limitation, AI agents can be employed to leverage existing EDA tools and gather domain/design-specific information, thereby facilitating more accurate error interpretation and debugging.
This presentation will showcase the application of AI agents in IBM's static structural checking tool, which is intensively used to ensure HDL compliance with IBM's design methodology. The tool operates on abstracted HDLs represented as a graph of BOXES and NETS, utilizing ANTLR grammar to traverse the graph. During traversal, the tool appends additional labels and tags to facilitate checking. However, the resulting error traces are often cryptic and require expert interpretation, slowing down the debugging process. By integrating AI agents, we aim to bridge the gap between tool errors and user understanding of the HDL, thereby accelerating debugging efforts by at least 30%. This innovative approach has the potential to significantly improve the efficiency and productivity of HDL verification and debugging processes.
Event Type
Engineering Poster
Networking
TimeWednesday, June 2512:15pm - 1:15pm PDT
LocationEngineering Posters, Level 2 Exhibit Hall