New Data Center Protocols Tackle AI


Compute nodes in AI and HPC data centers increasingly need to reach out beyond the chip or package for additional resources to process growing workloads. They may commandeer other nodes in a rack (scale-up) or employ resources in other racks (scale-out). The problem is there currently is no open scale-up protocol. So far this task has been dominated by proprietary protocols, because much of ... » read more

How Ultra Ethernet And UALink Enable High-Performance, Scalable AI Networks


By Ron Lowman and Jon Ames AI workloads are significantly driving innovation in the interface IP market. The exponential increase in AI model parameters, doubling approximately every 4-6 months, stands in stark contrast to the slower pace of hardware advancements dictated by Moore's Law, which follows an 18-month cycle. This discrepancy demands hardware innovations to support AI workloads, c... » read more