Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory costs and time-to-first-token by up to 8x for multi-turn AI applications.

Variability kills memory It’s tempting to draw parallels between emerging memory concepts and development trajectories for UltraRAM and other post-silicon semiconductor technologies. But that’s a ...

Step inside and you’re immediately struck by the multi-level layout that gives the space a complexity and interest that flat, ...

The research team led by Researcher Tianyu Wang from the School of Integrated Circuits at Shandong University has systematically reviewed the latest advances in emerging memristors for in-memory ...

'This is the steepest contraction in device shipments witnessed in over a decade'. When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works.

In the current AI landscape, we’ve become accustomed to the ‘ephemeral agent’—a brilliant but forgetful assistant that restarts its cognitive clock with every new chat session. While LLMs have become ...

Abstract: In-memory computing (IMC) for logic functions executes a target function via a series of logic operations supported by peripheral devices. Because these operations are performed on multiple ...

The memory-chip industry is surging because of generative AI related data center demand. This trend is great news for Micron. The stock's low valuation leaves room for continued growth. Despite the ...

AI workloads need to position more memory that uses less power in ever-closer proximity to computational logic. That overriding imperative is driving new memory designs and new materials exploration ...

A novel stacked memristor architecture performs Euclidean distance calculations directly within memory, enabling energy-efficient self-organizing maps without external arithmetic circuits. Memristors, ...