Industry Expert Blogs

Artificial intelligence (AI) accelerators are deployed in data centers and at the edge to overcome conventional von Neumann bottlenecks by rapidly processing petabytes of information. Even as Moore’s law slows, AI accelerators continue to efficiently enable key applications that many of us increasingly rely on, from ChatGPT and advanced driver assistance systems (ADAS) to smart edge devices such as cameras and sensors.

Although AI accelerators are typically 100x to 1,000x more efficient than general-purpose systems, the computational resources needed to generate best-in-class AI models doubles every 3.4 months. Moreover, training a single deep-learning model such as ChatGPT’s GPT3 creates approximately 500 metric tons of CO₂, the equivalent of over a million miles driven by an average gasoline-powered vehicle! To help reduce global carbon emissions, the U.S. Department of Energy (DoE) recently recommended a 1,000x improvement in semiconductor energy efficiency.

Achieving optimal performance-per-watt—whether for AI training in the data center or inference at the edge—is understandably a top priority for the semiconductor industry. In addition to minimizing environmental impact, reducing energy consumption lowers operating costs, maximizes performance within limited power budgets, and helps mitigate thermal challenges. Read on to learn how chip designers—including edge AI chip developer SiMa.ai—are leveraging end-to-end power analysis solutions to build a new generation of more energy-efficient AI accelerators.

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