Synopsys Foundation IP Enabling Low-Power AI Processors

By Shruti Bagaria, SoC Engineering, Foundation IP, Synopsys

Artificial Intelligence (AI) has become pervasive in recent years and has rapidly established itself as a transformative technology. AI is powered by machine learning (ML) algorithms, which require massive computational power. Designers have traditionally relied on graphics processing units (GPUs) to execute these ML algorithms. Originally developed for graphics rendering, GPUs have proven well suited for performing the matrix and vector operations essential to machine learning. However, the AI hardware landscape is undergoing dramatic changes. The increasing complexity of computational requirements and the need for improved energy efficiency are driving the emergence of startups specializing in domain-specific AI processors. These startups are developing specialized AI processors with architectures optimized for ML algorithms, delivering significantly improved performance per watt compared to general-purpose GPUs.

As AI technology continues to advance, the demand for greater computational power and energy efficiency will continue to increase. According to an analysis by Semianalysis (source: https://www.semianalysis.com/p/ai-datacenter-energy-dilemma-race), AI data center power needs are projected to surpass the non-AI data center power needs by 2028, accounting for more than half of global data center power consumption, compared to less than 20% today.

The data center industry is attempting to alleviate the power demand by moving away from traditional air-cooled systems and turning to more expensive but highly effective liquid cooling solutions. However, relying solely on advancements in external cooling is not enough. To manage these increasing power demands, AI hardware developers must also innovate within the system design itself, exploring more comprehensive avenues for power optimization.

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