Author : Saba Zargham 

High-speed interconnects are the physical links and protocols that enable rapid data transfer between chips, devices, or systems. They include standards like PCI Express (PCIe), Ethernet, InfiniBand, and proprietary links such as NVIDIA NVLink. These interconnects provide high bandwidth—often hundreds of gigabytes per second—at low latency to move data between processors, memory, storage, and network nodes, and are ubiquitous in modern electronics, from cloud data centers to consumer devices. They connect CPUs to accelerators, GPUs to GPUs, and servers to storage to accelerate data movement and prevent bottlenecks.

Interface IPs refer to pre-designed silicon intellectual property blocks that implement those interconnect standards (such as PCIe or Ethernet) and can be readily integrated into System-on-Chip (SoC) designs. These interface IP blocks typically comprise a digital controller and a physical (PHY) layer. The controller IP handles digital protocol execution, whereas the PHY IP is an analog/mixed-signal transceiver responsible for high-speed signal transmission and reception. It includes high-speed analog circuits to ensure signal integrity and meet the stringent timing requirements of modern interfaces. 

These IP blocks and interconnect technologies form the “glue” that allows advanced computing systems to communicate at extreme data rates.

The Rise of AI and Hyperscale Data Centers

The rapid rise of artificial intelligence (AI) workloads is transforming data center design, with AI-oriented capacity projected to grow by approximately 33% annually through 2030. Meeting this demand requires deploying massive clusters of accelerators at an unprecedented scale. These systems also draw enormous power. Take NVIDIA’s latest H100 GPU, for example—it can consume around 700 W. A 100,000-GPU supercluster (such as those reported at Meta) might require roughly 370 GWh of electricity per year—enough to power more than 34 million average American households. This combination of dense compute and extreme power consumption in AI infrastructure places tremendous strain on data movement and, as a result, on interconnects.

As workloads become more data-intensive, fast interconnects are essential for performance. In fact, modern AI and “big data” applications have created an insatiable demand for memory and interconnect bandwidth. Without fast and standardized interfaces, it would be impractical to scale systems or compose flexible infrastructures. Simply put, high-speed interconnects are the arteries of the digital world—vital for moving the “lifeblood” of data quickly and reliably.

Design Challenges in the Age of AI

Designing high-speed interfaces for the AI era presents significant technical challenges and pushes interconnect technology to its limits in terms of bandwidth, latency, and scale. One major challenge is simply keeping up with the demand for throughput. Whereas traditional data center networks doubled speeds approximately every four years, AI’s growth has accelerated that pace to nearly every two years. Interface standards are racing ahead (e.g., PCIe 6.0 to 7.0, Ethernet 800 G to 1.6 Tbps), but delivering ever-higher data rates is non-trivial. Signal integrity at multi-GHz frequencies over PCB traces or copper cables becomes more difficult due to attenuation and crosstalk. Engineers must employ advanced equalization, new materials, or move to fiber optics.

Power consumption is another concern—high-speed SerDes circuits and optical modules can consume substantial power, which, at the scale of hundreds or thousands of links, translates to significant energy usage and cooling demands. Achieving low latency and tight synchronization across large clusters is also difficult. Distributed training algorithms require that GPUs exchange messages frequently; any slowdown in the interconnect will stall the entire workload. Thus, designers strive to minimize latency in network switches and interface logic.


Future Directions

The foremost challenge in high-speed interface design for AI is scaling bandwidth and connectivity to match unprecedented data demands while maintaining low latency, manageable power, and system robustness. Solutions include moving to optical and advanced packaging for physical links, developing smarter network architectures, and fostering industry collaboration on open standards to ensure interoperability at scale. As generative AI continues to drive compute innovation, high-speed interconnects and interface IPs will remain a critical focal point—the enablers of the ever-larger models and datasets that define the modern AI revolution.