UMI Scales the Memory Wall in the Chiplet/Multi-Die Era

Discover how the Universal Memory Interface tackles substrate- and die-level real-estate challenges in chiplet-based design, unlocking performance and scalability.

By Ramin Farjadrad, Co-Founder and CEO, Eliyan

A cruel irony is occurring at an accelerating pace as we drive forward into the generative AI era: While the improvements in processor performance to enable the incredible compute requirements of applications like ChatGPT get all of the headlines, a not-so-new phenomenon known as the memory wall risks negating those advances. Indeed, it’s been clearly demonstrated that as CPU/GPU performance increases, wait time for memory also increases, preventing full utilization of the processors.

With the number of parameters in the generative-AI model ChatGPT-4 reportedly close to 1.4 trillion, artificial intelligence has powered head-on into the memory wall. Other high-performance applications aren’t far behind. The rate at which GPUs and AI accelerators can consume parameters now exceeds the rate at which hierarchical memory structures, even on multi-die assemblies, can supply them. The result is an increasing number of idle cycles while some of the world’s most expensive silicon waits for memory.

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