5 Key AI Predictions and Trends to Keep on Your Radar in 2022
By Stelios Diamantidis, Sr. Director, Synopsys AI Solutions, Office of the COO
From innovations in edge AI and computer vision to data modernization and specialized AI chips – with AI even designing some of those chips – there is no question that AI advancement has hit an all-time high in the past year. The global market value for AI chipsets is anticipated to grow to more than $70 billion by 2026, up $62 billion since 2019. This massive surge can be attributed to the swell of IoT devices with machine-learning capabilities and the evolution of smart cities.
As 2022 begins, so does a new era of technological innovation. With ongoing obstacles such as the global chip shortage, COVID-19 and supply chain challenges, the appetite for smart everything has put AI on all the big players’ wish lists.
Let’s dive into five key predictions on what we anticipate will usher in the next wave of AI development this year.
1. The Impact of AI in Chip Design Will Continue to Grow
With more workloads requiring advanced levels of AI processing to power intelligent functions, the need for dedicated chips that are energy-efficient and perform computations at higher speeds will make robust AI chip designs paramount. A new breed of design tools that learn from iterations and leverage data in chip design environments offer a leap forward in productivity and cost efficiency. The AI disruption will open new opportunities not only for the semiconductor leaders but also for companies that typically had smaller teams or more limited financial resources and weren’t previously seen as powerhouses of chip design to deliver exciting silicon solutions ― leveling the playing field, in a sense, and creating a perfect symmetry of companies leveraging AI in chip design across the global economy.
Designing the AI hardware of the future will require a revolution in chip design technology. In 2021, companies that dared to invest in the data center market witnessed significant gains and demonstrated impressive technical capabilities that increased the demand for dedicated AI chips, raising AI funds at an unprecedented pace. With GPUs continuing to be the dominant architecture for training in the data center market, we expect this growth to continue and see hyperscalers opting for next-generation AI-assisted design systems to massively scale exploration design workflows while automating less consequential decisions. Companies will start pivoting to the cloud for their chip design needs ― a victory for additional capacity, faster turnaround times, and high-quality application-optimized design.
