AI Applications: The New Frontier of Chip Design

Semiconductor Review | Wednesday, March 29, 2023

AI chip design is advancing to meet the demand for efficient computing power, driving innovation and enabling growth in AI applications.

FREMONT, CA: The proven solution that enhances engineering productivity and quality of results is making it possible to accomplish. This comes incidentally from one of the very technology that is driving greater chip complexity artificial intelligence (AI)

Engineering inventiveness has led to advancements like AI-powered chatbots, surgery-performing robotics, and self-driving cars. It has also produced solutions that discharge repetitive chip design, verification, and testing tasks, allowing engineers to focus on what they do best in innovation.

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AI application for chip design leads the way as AI-driven electronic design automation (EDA) design suite, with the solution for functional verification and silicon test. There is an impressive result from a 10x improvement in reducing functional coverage holers up to a 30 per cent increase in IP verification productivity.

AI in the EDA Workflow Tackles Iterative and Manual Tasks

The semiconductor industry has become increasingly competitive in an uncertain global economy. As a result, there is a greater need for improved semiconductor power, performance, and area metrics. Companies are striving to optimise these metrics more efficiently and cost-effectively to gain an edge. However, there are underlying staffing challenges, with projections of a shortage of semiconductor engineering talent that could impede progress in the industry. By 2030, it is expected that the semiconductor design industry could face a deficit of 23,000 semiconductor engineers.

Semiconductor companies are increasingly investing in AI-driven EDA design tools to complement their engineering teams. It is projected that these companies will spend around USD 300 million on internal and third-party AI tools to design chips in 2023, with a growth rate of 20 per cent per year over the next four years. AI design tools are proving beneficial in enabling chipmakers to push the limits of Moore's law, save time and money, address talent shortages, and update older chip designs. These tools can also increase supply chain security and help mitigate potential chip shortages.

AI technology can perform repetitive and time-consuming tasks such as design space exploration, verification coverage, and test pattern generation. This allows engineering teams to focus on more value-added tasks and meet aggressive time-to-market targets while using existing resources. Both new and experienced engineers can benefit from AI, as it provides a faster ramp-up time for new engineers and enables experienced engineers to achieve better quality results in less time. With the help of AI tools, engineers can work more efficiently by delegating repetitive tasks and overseeing chip quality to generate better and faster results than what would be possible by human effort alone.

Smarter Chips to Solve Complex Challenges

Chip designers are encountering a multitude of obstacles such as the rising intricacy of designs, demanding requirements for advanced process nodes, and reducing power budgets for various types of chips including CPUs, mobile, GPUs, automotive, and AI. For instance, in the case of AI chips, they require multiple computing units to handle complex algorithms and vast amounts of data, whether in servers located in data centres or edge devices. It is a challenging task to optimise these complex designs for power, performance, and area (PPA) and to ensure that they will perform as expected.

The field of artificial intelligence (AI) is indeed driving a new frontier in chip design. With the increasing demand for advanced AI applications and the need for efficient computing power, chip designers are constantly exploring new technologies and techniques to design more powerful and energy-efficient chips. The insertion of AI chip design is a promising area of innovation that is driving the development of more powerful and efficient chips, which in turn is enabling the growth and advancement of AI applications in various industries.

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