AI is currently just scratching the surface of the impact that’s possible in the electronic design space. The panelists at SNUG Silicon Valley 2023 agreed that with rise of natural language models such as those seen in AI chatbots like ChatGPT, as well as other opportunities presented by AI, now is an exciting time to be in the industry. There’s more to be done to advance autonomous design, verification, and test, as well as more areas to enhance. Strong electronic design automation (EDA) technologies with a tightly integrated, machine learning-driven loop can be a powerful force enabling engineers to accomplish more than ever possible.
“With the move to FinFET nodes, new problems are emerging,” said Vuk Borich, distinguished architect, Synopsys Circuit Design & TCAD Solutions. “While chips are denser and smaller and there’s more of them, there’s some regularity and some patterns and some things that are amenable to artificial intelligence. So we foresee a great deal of innovation.”
Just looking at analog design, one can pinpoint areas that can benefit from an infusion of intelligence. As Borich highlighted:
- Billions of Monte Carlo simulations must be performed to assess process variability. Are there ways to reduce the time and cost of these simulations using AI?
- Extracting parasitics takes hours or days. With hundreds of design parameters and longer iterations, can analog design closure time be shortened with AI?
- Layout represents a substantial manual effort: can AI streamline this process, especially when the talent to do this work is in limited supply?
Beyond electronic systems, another area where AI can accelerate convergence with less designer intervention is in optical design. Optical design is a key enabling technology for applications such as imaging, automotive illumination, and photonic ICs. These applications are highly complex, with a large number of variables and tolerances to consider which were historically handled with special tools. AI has the potential to unlock new opportunities to co-optimize specialized algorithms, explained William Cassarly, a Synopsys Scientist on the optical solutions team. AI allows exploration of a large portion of the design space, provides new starting points for existing algorithms, and reduces the effort involved in handling discrete cases. In addition, AI offers the potential to enable knowledge transfer between completely different use cases, allowing less experienced designers to produce results that might have only been considered by a designer with significant experience.
As we approach the system level, siloed knowledge across hardware and software teams makes bring-up a complex and costly effort. System-level visibility and automated root-cause analysis are key to faster time to market. Rachana Srivastava, a senior staff R&D engineer in the Synopsys Systems Design Group, noted that AI can enable automated system-level root-cause analysis. Mapping data in an event-based knowledge graph can provide visibility across the system. Applying machine-learning models on this data can generate predictions and a feedback loop for information mining to generate better silicon results.
Exciting times are indeed to come as engineers devise new ways to apply AI and machine learning to workflows across the system stack. Designs that meet PPA and time-to-market targets for next-generation applications will only grow more complex. AI can provide the productivity boost that engineering teams need, while helping them achieve outcomes that were previously unimaginable.