PCIe Benefits From AI, Despite Scaling Protocols


Key takeaways: PCIe remains a critical technology for non-AI processing. For AI, PCIe will be strengthened by scale-out, agentic AI, and even some scale-up. CXL is seeing uptake, and some even think it could participate in AI processing. PCIe has been the go-to network for most data traffic moving from a processor to devices located elsewhere, which is also what the new data... » read more

Confusion Grows With More Interconnect Options And Tradeoffs


Key Takeaways: Designers are frequently evaluating 5 or more different interconnects in a single system, each with a distinct purpose. While chip-to-chip (PCIe) and die-to-die (UCIe, BoW) technologies seem to be solving a similar problem, in practice they bring different challenges. PCIe, CXL, NVLink, and UALink are all active in the hyperscaler space, but Ethernet-based technologies... » read more

Verifying Scale-Up And Scale-Out In Data Centers


Semiconductor Engineering sat down to discuss challenges and solutions for data center build-out and build-up with Gordon Allan, Siemens EDA director of verification IP; Rishi Chugh, vice president of product marketing for network switching at Marvell; Saravanan Kalinagasamy, senior director of ASIC design and validation at Astera Labs; and Jalaj Gupta, product engineering lead at Siemens EDA. ... » read more

Securing UALink: Introducing A UALinkSec Security Module


By Dana Neustadter and Vincent van der Leest Over the next decade, artificial intelligence will dominate computing, driving profound changes in both hardware and software architectures. This transformation will reshape data center design and interconnect technologies, creating new opportunities for innovation. As AI workloads scale, the need for high-speed, low-latency communication between ... » read more

Multiple AI Scale-Up Options Emerge


Artificial intelligence (AI) workloads are very different from those traditionally run inside of data centers, and while the current infrastructure can accommodate those needs, there is a constant demand for higher performance and better power efficiency. It can take months to train a large language model, even with a huge number of processing elements. Typically this involves commandeering ... » read more

UALink: Powering The Future Of AI Compute


On April 25, the UALink Consortium officially released the UALink 200G 1.0 Specification, marking an important milestone with support from key hyperscalar market players. It enables a low-latency, high-bandwidth fabric that supports hundreds of accelerators in a pod and facilitates simple load-and-store semantics. Motivation behind UALink The rapid evolution of Artificial Intelligence (AI) an... » read more

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