Fabric Emulator

The Fabric Emulator product brings the realism of customer networks into experimentation and test workflows, for developers of the next generation of system components.

Advance AI Fabric Validation and Workload Emulation at 1600GE

Learn how to validate next-generation 1.6T Ethernet AI fabrics with greater confidence. This interactive webinar explores AI workload emulation, 224G physical layer validation, RoCEv2 traffic testing, congestion analysis, and full-stack visibility to identify performance issues earlier, reduce deployment risk, and optimize AI cluster efficiency.

Beyond Throughput: Why Stateful RoCEv2 is the Heart of AI Fabric Validation

Learn about how stateful RoCEv2 testing is critical for AI fabrics, emulating realistic congestion and retransmission to uncover bottlenecks and ensure predictable performance

Ethernet Traffic Emulators Catalog

This catalog highlights Keysight’s Ethernet traffic emulation portfolio, including L2–L7 protocol and load test software and high‑performance hardware accelerators for validating next‑generation networking systems, ASICs, AI data center environments, and hyperscale deployments with realistic, repeatable traffic at speeds up to 1.6T.

Maximizing available buffers: Using a shared buffer

Extending total buffer availability of fabric by changing the default memory allocations for the ports used in the experiment.

Maximizing available buffers: Memory partitions

Increasing total buffer availability by dividing the front panel ports evenly between the ASIC’s two memory partitions.

Validating AI Fabric Without GPUs: NCCL Emulation in Practice

The post explains how NCCL collective communication patterns can be emulated at the network level to validate AI fabrics without GPUs

Eight priorities

Fabric Emulator emulates a switch topology with PFC enabled and 8 priority groups configured. Followed by verification of the 8 priority groups being in use by traffic flows.

AI Fabric Test Methodology

In the context of Artificial Intelligence (AI) systems, particularly Large Language Models (LLMs), a network fabric refers to the underlying infrastructure that connects and enables communication between various components or nodes within the system. The network fabric is crucial in facilitating efficient and effective AI system operation, including LLM training.

Congestion control with PFC and ECN

Fabric Emulator used to emulate an 8 switch topology for the purpose of demonstrating how a combination of PFC and ECN can be used to manage network congestion more effectively.

Fabric Controller Centralized Manager

To cope with skyrocketing traffic volume and escalating security threats, today’s networks employ sophisticated, pervasive monitoring capabilities. The resulting architecture incorporates dozens – or even hundreds – of taps and bypasses, data monitoring switches, and Network Packet Brokers (NPB), all integrated into a Monitoring Access Platform (MAP) within the network infrastructure.

Interconnect Fabrics and SDN: AI-Centric Evolution

Deepak Bansal, Corporate Vice President at Microsoft, explores how interconnect fabrics and software-defined networking (SDN) stacks are evolving to meet the needs of AI data centers.

Keysight AI Fabric RoCEv2 Test Solution

Keysight’s AI Fabric Test Solution includes a high-density cost-effective test hardware platform (AresONE-M 800GE or AresONE-S 400GE) and the IxNetwork test application. IxNetwork models the AI training workload running on the tester’s target topology.

Scale-Up Ethernet and UALink Validation for AI Fabrics

AI scale-up fabrics rely on high-bandwidth interconnects to move data efficiently between accelerators, processors, switches, and memory. This paper examines validation methodologies for 200 Gb/s Ethernet and UALink interfaces, including channel characterization, transmitter compliance, receiver stress testing, and interoperability considerations for reliable AI infrastructure deployment.

EP-Scan for Fabrication-Aware Design Checks

Ensure design robustness by evaluating performance under fabrication variation, enabling validation against realistic manufacturing conditions.

Demo — AI Data Center Fabric Test Methodology

Keysight’s AI fabric test methodology helps optimize AI data center performance by emulating GPU traffic, benchmarking collective operations, and fine-tuning congestion control. It enables scalable, automated testing to identify bottlenecks and configure backend networks for reliable, high-throughput AI workloads.

Emulating Multipath Effects with Radar Scene Emulators

This is the app note version of a scientific peer-review publication authored by Keysight Germany solution engineer Arvid Sims in collaboration with researchers Bernhard Holzinger and Tom Vandeplas from Keysight Germany, and Keysight Belgium, respectively. It has been presented at ICMIM 2024; 7th IEEE MTT Conference, in Boppard, Germany, in April 2024.

How to Use the Emulator Function in Keysight's Battery Emulator and Profiler

In this video, we will show you how to use the emulator function using the Keysight Battery Emulator and Profiler (E36731A) along with the PathWave Advanced Battery Test and Emulation (PW9253A).

E36731A Battery Emulator

The E36731A is designed to operate with Keysight BV9211B Pathwave BenchVue advanced battery emulation software. This combination allows you to easily profile batteries and perform battery emulation, enabling you to perform battery drain analysis at different charge levels.

Network Emulators Catalog

This catalog highlights Keysight’s Network Emulator 2 and Network Emulator 3 solutions, enabling precise replication of real‑world network impairments across 100 MbE to 100GE Ethernet. It provides performance details, use cases, architecture capabilities, and supporting tools for validating application robustness, interoperability, and network resilience before deployment.