Networking startup Enfabrica is actively participating in trade expos to showcase its latest networking offerings, aimed at managing the substantial data demands of AI.

The core technology in focus is their Accelerated Compute Fabric SuperNIC (ACF-S) chips, crafted to boost bandwidth, enhance resiliency, reduce latency, and offer more control programmatically to operators in data centers dealing with AI and high-performance computing tasks.

Emerging from stealth mode the previous year, Enfabrica announced securing $125 million from a funding initiative spearheaded by Atreides Management, joined by Nvidia – which competes in the smartNIC market with its BlueField series, along with multiple venture capital firms.

Founded in 2020 by Shrijeet Mukherjee, former leader of networking platforms and architecture at Google, and CEO Rochan Sankar, who served as a director of engineering at Broadcom, they identified and targeted what they consider major inefficiencies in networking hardware stemming from outdated designs that serve CPUs well but fall short for GPU networking requirements.

“Data center networking has evolved into a design that allows incoming traffic to be distributed across numerous nodes. However, AI and ML systems present new challenges,” stated Mukherjee, the chief development officer.

Enfabrica identifies a critical issue in traditional data center setups, where the expansion of server networking components and restricted connectivity impede bandwidth and fault tolerance. AI applications demand extensive data transfers across GPUs, which often involve multiple transitions, leading to congestion and uneven load distribution. A single GPU link failure can halt the entire process.

“The architecture of contemporary supercomputers lacks sufficient fault tolerance, necessitating significant effort to manage failures effectively,” Mukherjee commented.

Enfabrica enhances fault tolerance in network design by setting up numerous routes between any two points, facilitating load balancing. If a failure occurs, the system automatically reallocates the tasks among the remaining links.

“Data centers are traditionally designed around dual-socket systems, which are highly efficient as long as operational needs are confined within these parameters. Once needs exceed these boundaries, efficiency declines,” explained Mukherjee.

“We’ve determined a need to overhaul the architecture itself to address these inefficiencies,” Mukherjee continued. “It’s essential for us to be a silicon-based company that epitomizes and accelerates the development of modern systems.”

ACF-S facilitates multi-terabit switching and bridging across different compute and memory resources via a single silicon die, which simplifies physical interfaces without altering protocols or upper software layers beyond device drivers. This enhancement leads to fewer components, reduced I/O latency, and lower power consumption in AI clusters which are generally strained by various network and storage interfaces.

The technology also promotes headless memory scaling to any accelerator, which significantly enhances the memory access capabilities of a single GPU rack, providing direct, swift, and exclusive access to local CXL.mem DDR5 DRAM. This access vastly exceeds the memory capacities traditionally available to GPUs through High-Bandwidth Memory (HBM).

Enfabrica showcased its innovations at various technology events such as Hot Chips, AI Summit, and AI Hardware & Edge AI Summit, including an appearance at Gestalt IT AI Tech Field Day. The company is also preparing for its participation at the SuperComputing 2024, scheduled for Nov. 17-22 in Atlanta.

There is no confirmed date from Enfabrica regarding when their products will be available in the market.

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