In 1994, University of Virginia computer science professor emeritus William Wulf and his then-graduate student, Sally McKee, identified what would become a defining challenge in the field of computer ...

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

The term “memory wall” was first coined in the 1990s to describe memory bandwidth bottlenecks that were holding back CPU performance. The semiconductor industry helped address this memory wall through ...

While the improvements in processor performance to enable the incredible compute requirements of applications like Chat-GPT get all the headlines, a not-so-new phenomenon known as the memory wall ...

Shimon Ben-David, CTO, WEKA and Matt Marshall, Founder & CEO, VentureBeat As agentic AI moves from experiments to real production workloads, a quiet but serious infrastructure problem is coming into ...

What if the future of artificial intelligence is being held back not by a lack of computational power, but by a far more mundane problem: memory? While AI’s computational capabilities have skyrocketed ...

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 bottleneck in AI and other memory-intensive applications whereby the transfer of data to and from memory is the slowest operation. For example, CPU register and cache cycle times are less than one ...

Artificial intelligence has been bottlenecked less by raw compute than by how quickly models can move data in and out of memory. A new generation of memory-centric designs is starting to change that, ...