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 focus: memory. Not compute. Not models. Memory.

Today is the ribbon-cutting ceremony for the “Venado” supercomputer, which was hinted at back in April 2021 when Nvidia announced its plans for its first datacenter-class Arm server CPU and which was ...

Micron, SK Hynix and Samsung Electronics — make up nearly the entire RAM market, and they're benefitting from this shortage.

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 ...

Judged by the presence of artificial intelligence (AI) and machine learning (ML) technologies at 2023 Design Automation Conference (DAC), the premiere event for semiconductor designers, computing ...

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

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 ...

D.A. Davidson analyst Gil Luria told MarketWatch that Intel’s stock move could be further indication that investors are ...

Processor performance continues to improve exponentially, with more processor cores, parallel instructions, and specialized processing elements, but it is far outpacing improvements in bandwidth and ...