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Chiplet design basics for engineers
Jul. 28, 2025 –
The world is experiencing an insatiable and rapidly growing demand for artificial intelligence (AI) and high-performance computing (HPC) applications. Breakthroughs in machine learning, data analytics, and the need for faster processing across all industries fuel this surge.
Application-specific integrated circuits (ASICs), typically implemented as system-on-chip (SoC) devices, are central to today’s AI and HPC solutions. However, traditional implementation technologies can no longer meet the escalating requirements for computation and data movement in next-generation systems.
From chips to chiplets
Traditionally, SoCs have been implemented as a single, large monolithic silicon die presented in an individual package. However, multiple issues manifest as designers push existing technologies to their limits. As a result, system houses are increasingly adopting chiplet-based solutions. This approach implements the design as a collection of smaller silicon dies, known as chiplets, which are connected and integrated into a single package to form a multi-die system.
For example, Nvidia’s GPU Technology Conference (GTC) has grown into one of the world’s most influential events for AI and accelerated computing. Held annually, GTC brings together a global audience to explore breakthroughs in AI, robotics, data science, healthcare, autonomous vehicles, and the metaverse.
During his GTC 2025 keynote, Nvidia president, co-founder, and CEO Jensen Huang emphasized the need for advanced chiplet designs, stating: “The amount of computation we need as a result of agentic AI, as a result of reasoning, is easily 100 times more than we thought we needed this time last year.”
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