Micron's next AI memory breakthrough has a cooling problem

Micron Technology, Inc‘s MU next memory architecture could help AI processors move data faster, but getting more memory ...
Wccftech

While Micron Calls The 4F² Memory Cell Architecture A “Myth,” YMTC Has Followed CXMT In Lining Up A Prototype That Operates At 9,600 MT/s

Micron's Scott DeBoer says 4F² has 'fundamental problems' with scaling, even as CXMT and YMTC appear to be going all in on this technology.
Wccftech

CXMT To Leapfrog Samsung By ~2 Years, As It Gears Up To Introduce A DDR5 RDIMM Product That Leverages The 4F² Memory Cell Architecture Before Year-End

CXMT published its first 4F² paper back in 2021, though its initial version will likely be a partial derivative on the new 15nm G5 process.
VentureBeat

GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs

For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning ...

XCENA Showcases CXL Memory Innovations for AI Inference at OCP Global Summit 2026

XCENA, a fabless semiconductor company developing next-generation memory infrastructure for the AI era, demonstrated its ...
TechNode

CXMT plans next-generation DDR5 server memory using denser DRAM architecture

Chinese memory maker CXMT plans to launch a new generation of DDR5 registered memory modules by the end of 2026 using its 4F² DRAM architecture. President ...
SiliconANGLE

New memory architecture targets AI inference bottlenecks

Lightbits Labs Ltd. today is introducing a new architecture aimed at addressing one of the most stubborn bottlenecks in large-scale artificial intelligence inference: the growing mismatch between the ...
VentureBeat

Google's new neural-net LLM architecture separates memory components to control exploding costs of capacity and compute

A new neural-network architecture developed by researchers at Google might solve one of the great challenges for large language models (LLMs): extending their memory at inference time without ...