Quick recap
Michelle and Matthew presented about Artificial Intelligence and Machine Learning Role in Register Transfer Logic Design Generation.
Summary
AI/ML in RTL Design Generation
In the meeting, Michelle and Matthew presented about the potential of artificial intelligence and machine learning in the Electronic Design Automation (EDA) and Register Transfer Logic (RTL) design generation process. They identified core concepts for RTL design with AI/ML from an open-source perspective and discussed the importance of mitigating risks such as schedule, technical, and cost risks in the design process. Matthew highlighted the iterative nature of the design process and the need for early identification of design deficiencies and incorrect assumptions. He also suggested the use of AI agents to guide the high-level synthesis process, assist in design space exploration, and improve the placement process.
Open-Source EDA and Workforce Challenges
Michelle discussed the potential of open-source EDA and recommendations from a European white paper, highlighting the challenges of limited access to EDA software and the workforce shortage. She mentioned the positive impact of open-source initiatives like the RISC-V processor and the Google Skywater Process Design Kit (PDK). The recommendations included focusing more on analog and mixed-signal designs, interoperability and verification, and system-on-chip integration. She also emphasized the importance of proper licensing, funding, sustainability, and industry training for both open-source and AI/ML projects. Lastly, she mentioned the need for more conferences and events to better understand and filter the vast amount of information in the field.
Large Language Models in Design IP
Michelle discussed the use of large language models and automated writing of HDL for design IP. She shared her experience with Matlab's HDL Coder, which can produce high-quality, human-readable HDL code but requires a lengthy process and is not suitable for complex monolithic designs. She also mentioned the use of AI and ML in deep learning hardware models and their potential to inform the design process. Michelle also presented a survey of experts in the field, which showed a bias towards high-quality from proprietary tools and lower quality expectations from open source and AI/ML tools. Matthew then discussed relevant papers he had found in his literature search, focusing on areas such as RTL generation, hardware verification, testbench generation, and formal verification. He emphasized the potential of these papers to inform and improve the design process.
AI/ML Applications in Analog Design
Matthew presented about the role of of AI/ML in analog design, signal processing, and network planning. He suggests that AI tools can help with analog design, potentially enabling non-specialists to experiment and learn. Matthew also discusses the use of AI/ML in detecting convolutional codes, channel estimation, and radio map generation for network planning. Michelle adds that there is significant overlap with information theory in these applications, particularly in tracking entropy levels in codes and signals. She mentions upcoming presentations on this topic at the Information Theory and Applications Conference. Michelle also highlights successful technology demonstrations by DARPA in 2019 showing improved spectrum efficiency using AI/ML models.
EDA Tools: Proprietary vs Open-Source
Daniel, Michelle, and Matthew discussed the pros and cons of using proprietary versus open-source Electronic Design Automation (EDA) tools. Michelle highlighted the challenges of open-source projects, such as the lack of funding, limited market size, and difficulty in maintaining quality. Matthew shared his experience with open-source simulators, noting their limitations in terms of feature set parity and language coverage. Daniel suggested that open-source solutions might take longer to develop but could benefit from advancements in AI tools. Michelle and Matthew agreed that the EDA market is small, which contributes to the high cost of proprietary tools. They concluded that while there are good open-source projects, they may not be comprehensive enough for complex tasks.
Exploring AI, ML, and Open-Source Projects
In the meeting, Matthew and Michelle discussed the potential of AI and ML in the field of digital design, particularly in the context of open-source projects. Matthew expressed his excitement about the opportunities and his desire to learn more about these tools. Michelle emphasized the importance of open-source projects for personal and professional development, and the need for more people to contribute to these projects. They also discussed the possibility of organizing more meet-ups to explore specific frameworks and resources. The conversation ended with an invitation for further questions and a promise to distribute the slides and recording link.