Achieving Reliable 2m+ DAC Connectivity For AI Scale Networks With 224G PHY IP


As artificial intelligence workloads and hyperscale data centers continue to evolve, the requirements for networking infrastructure are becoming increasingly stringent. High-speed, reliable connectivity is essential to support the massive data flow and low-latency demands of AI-scale environments. Passive direct attach copper (DACs) remains an attractive choice for hyperscalers and system vendo... » read more

Data Centers Boost Voltage For Higher Efficiency


The power architecture used in HPC and AI data centers today is about to undergo a significant change in an effort to boost power efficiency. While voltages at the chip level will remain the same, the voltages leading to those chips will be kept higher for longer distances. This change has broad implications for DC-DC converters. The existing architecture brings AC to each rack, converts it ... » read more

Developing Next-Generation Integrated Optical Engines


By Susan Coleman and Emily Gerken Data demand is soaring worldwide as high-resolution video streaming, virtual reality, the Internet of Things (IoT), high-performance computing (HPC), and artificial intelligence and machine learning (AI/ML) drive an insatiable appetite for data. As a result, networks and data centers face increasing pressure to expand bandwidth, reduce latency, and lower pow... » read more

Optimizing Optical Fiber Connections In Hyperscale Datacenters: A Simulation-Driven Approach


Hyperscale datacenters are redefining the limits of data transmission technology, driven by increasing demands for higher bandwidth, lower power consumption, and reduced latency. Through advanced simulation workflows and multiphysics integration, engineers can design, optimize, and validate optical coupling systems for next-generation datacenters. The use of Ansys Optics software for photonic c... » read more

Beyond the Bottleneck: AI Cluster Networking Report 2025


Artificial intelligence (AI) is the engine of next-generation innovation. However, increasing complexity means increased demand on data center networks. As AI grows into a central component of enterprise strategies, organizations must carefully consider how they design, test, and scale their infrastructure. This report, based on a global survey conducted by Heavy Reading in collaboration with K... » read more

Rethinking AI Infrastructure: The Rise Of PCIe Switches


When thinking of AI, images of futuristic robots or self-driving cars may come to mind. What might not come to mind are the unsung hardware component heroes that are quietly enabling such complex systems. Among these, PCI Express (PCIe) switches might seem to be a boring topic to write about, much less read. But here's the twist—they are nothing short of revolutionary when it comes to empower... » read more

Server-Scale Programmable Photonic Fabric to Interconnect Accelerators Within Servers (Cornell University, Lightmatter)


A new technical paper titled "Morphlux: Programmable chip-to-chip photonic fabrics in multi-accelerator servers for ML" was published by researchers at Cornell University and Lightmatter. Abstract "We optically interconnect accelerator chips (e.g., GPUs, TPUs) within compute servers using newly viable programmable chip-to-chip photonic fabrics. In contrast, today, commercial multi-accelerat... » read more

System-Level Design For 1.6 Tbps Interoperability In AI Data Centers


By Madhumita Sanyal and Diwakar Kumaraswamy The rapid escalation of AI/ML workloads—driven by increasingly large language models—is reshaping high-performance computing and AI data center architectures. Real-time inference and large-scale training are pushing the limits of compute and interconnect performance. With model sizes and parameter counts doubling every 4–6 months, infrastruct... » read more

Re-Architecting AI For Power


The industry is becoming increasingly concerned about the amount of power being consumed by AI, but there is no simple solution to the problem. It requires a deep understanding of the application, the software and hardware architectures at both the semiconductor and system levels, and how all of this is designed and implemented. Each piece plays a role in the total power consumed and the utilit... » read more

Maximize Uptime And Improve TCO: RAS And Telemetry In HBM4 For Data Centers


As AI workloads scale and data center operations become increasingly complex, it is critical to keep the infrastructure up and running. Total Cost of Ownership (TCO) is a key metric that includes not only the upfront cost of hardware but also the ongoing expenses of power, cooling, maintenance, and—most importantly—downtime. A single memory failure in a hyperscale AI cluster can cascade int... » read more

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