The growing imbalance between the amount of data that needs to be processed to train large language models (LLMs) and the inability to move that data back and forth fast enough between memories and ...

The biggest challenge posed by AI training is in moving the massive datasets between the memory and processor.

As agentic AI moves from experiments to real production workloads, a quiet but serious infrastructure problem is coming into ...

A Nature paper describes an innovative analog in-memory computing (IMC) architecture tailored for the attention mechanism in large language models (LLMs). They want to drastically reduce latency and ...

Artificial intelligence computing startup D-Matrix Corp. said today it has developed a new implementation of 3D dynamic random-access memory technology that promises to accelerate inference workloads ...

Researchers propose low-latency topologies and processing-in-network as memory and interconnect bottlenecks threaten inference economic viability ...

Researchers have created a new kind of 3D computer chip that stacks memory and computing elements vertically, dramatically speeding up how data moves inside the chip. Unlike traditional flat designs, ...

Artificial intelligence has raced ahead so quickly that the bottleneck is no longer how many operations a chip can perform, but how fast it can feed itself data. The long-feared “memory wall” is now ...

Artificial intelligence has raced ahead so quickly that its biggest constraint is no longer clever algorithms but the plumbing that feeds them data. A new generation of 3D chips, built by stacking ...