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Chipmakers are utilizing both evolutionary and revolutionary technologies to achieve orders of magnitude improvements in performance at the same or lower power, signaling a fundamental shift from manufacturing-driven designs to those driven by semiconductor architects.

In the past, most chips contained one or two leading-edge technologies, mostly to keep pace with the expected improvements in lithography at each new process node every couple of years. Those improvements were based on industry roadmaps that called for predictable but unremarkable gains over time. Now, with the explosion in data fueled by large language models and more sensors — as well as increased competition between systems companies designing their own chips, and growing international rivalries involving AI — the rules are changing rather dramatically at the leading edge of chip design. Incremental improvements now are being coupled with giant leaps in processing performance, and while those enable a whole new level of compute capabilities and analytics, they also require a whole new set of tradeoffs.

At the heart of these shifts are highly customized chip architectures, some involving chiplets developed at the most advanced process nodes. Parallel processing is almost a given, and so are accelerators aimed at specific data types and operations. In some cases, these mini-systems will not be sold commercially because they are what gives data centers their competitive edge. But they also may include other commercially available technology, such as processing cores or accelerators or in- or near-memory compute technology to reduce latency, as well as different caching schemes, co-packaged optics, and much faster interconnects. Many of these developments have been in research or sitting on the sidelines for years, and they are now being fully deployed.

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