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From Wikipedia, the free encyclopedia

Use of artificial intelligence in the automation of electronic design

AI-driven design automation is the use of artificial intelligence (AI) to automate and improve different parts of the electronic design automation (EDA) process. It is particularly important in the design of integrated circuits (chips) and complex electronic systems, where it can potentially increase productivity, decrease costs, and speed up design cycles. AI Driven Design Automation uses several methods, including machine learning, expert systems, and reinforcement learning. These are used for many tasks, from planning a chip's architecture and logic synthesis to its physical design and final verification.

This figure explains how can we train large circuit models by making use of the front-end (depicted in blue) and back-end (depicted in yellow) of the EDA flow in order to either enhance existing EDA tools or to create novel EDA applications.[1]

History<br>[edit]

1980s–1990s: Expert systems and early experiments<br>[edit]

The use of AI for design automation originated in the 1980s and 1990s, mainly with the creation of expert systems. These systems tried to capture the knowledge and practical rules used by human design experts, and used these rules, along with reasoning engines, to direct the design process.[2]

A notable early project was the ULYSSES system from Carnegie Mellon University. ULYSSES was a CAD tool integration environment that let expert designers turn their design methods into scripts that could be run automatically. It treated design tools as sources of knowledge that a scheduler could manage.[3]

Another example was the ADAM (Advanced Design AutoMation) system at the University of Southern California, which used an expert system called the Design Planning Engine. This engine figured out design strategies on the fly and handled different design jobs by organizing specialized knowledge into structured formats called frames.[4]

Other systems like DAA (Design Automation Assistant) used a rule-based approach for specific jobs, such as register transfer level (RTL) design for systems like the IBM 370.[2] Researchers at Carnegie Mellon University also created TALIB,[5] an expert system for mask layout that used over 1200 rules, and EMUCS/DAA for CPU architectural design which used about 70 rules. These projects showed that AI worked better for problems where relatively few rules were required to describe much larger amounts of data. At the same time, there was a surge of tools called silicon compilers like MacPitts,[6] Arsenic,[7] and Palladio.[8] They used algorithms and search techniques to explore different design paradigms. This was another way to automate design, even if it was not always based on expert systems.[9] Early tests with neural networks in VLSI design also happened during this time, although they were not as common as systems based on rules.

2000s: Introduction of machine learning<br>[edit]

In the 2000s, interest in AI for design automation increased. This was mostly because of better machine learning (ML) algorithms and more available data from design and manufacturing. For example, they were used to model and reduce the effects of small manufacturing differences in semiconductor devices. This became very important as the size of components on chips became smaller. The large amount of data created during chip design provided the foundation needed to train smarter ML models. This allowed for predicting outcomes and optimizing in areas that were hard to automate before.

2016–2020: Reinforcement learning and large scale initiatives<br>[edit]

A major turning point happened in the mid to late 2010s, sparked by successes in other areas of AI. The success of DeepMind's AlphaGo in mastering the game of Go inspired researchers. They began to apply reinforcement learning (RL) to difficult EDA problems. These problems often require searching through many options and making a series of decisions.

In 2018, the U.S. DARPA started the Intelligent Design of Electronic Assets (IDEA) program.[10] A main goal of IDEA was to create a fully automated layout generator that required no human intervention, able to produce a chip design ready for manufacturing from RTL specifications in 24 hours. Another big initiative was the OpenROAD project, a large effort under IDEA led by UC San Diego with industry and university partners, aimed to build an open source, independent toolchain. It used machine learning, parallelization and divide and conquer approaches.[11]

A much-publicized but controversial demonstration of RL's potential came from Google researchers between 2020 and 2021. They created a deep reinforcement learning method for planning the layout of a chip, known as floorplanning....