Keysight Technologies has unveiled a new suite of tools, known as Keysight Artificial Intelligence (KAI), aimed at enhancing the efficiency of AI data center deployments. These tools are designed to pinpoint key performance inhibitors that could negatively impact large GPU installations. Rather than using actual resources, KAI emulates workload profiles to identify performance bottlenecks.
To successfully scale AI data centers, comprehensive testing is required at each stage, from design through construction. Each component, including chips, cables, interconnects, switches, servers, and GPUs, must be validated throughout the process. KAI is directly aimed at identifying weaknesses that might degrade the performance of these data centers, optimizing system-level performance to ensure smooth scaling and increased throughput.
The architecture integrates several tools designed specifically for the AI data center development and testing lifecycle. KAI includes the new Data Center Builder for simulating large-scale AI workload scenarios. Its four core product suites address various aspects of AI data center design:
- KAI Compute: Optimizes digital design in next-generation AI chip development.
- KAI Interconnect: Validates optical and electrical data paths.
- KAI Network: Benchmarks the performance of AI networks.
- KAI Power: Optimizes power efficiency and manages energy across data center components.
According to Ram Periakaruppan, Keysight’s VP and general manager of network test and security solutions, scaling AI data centers mandates more than isolating component-level validations. Effective assessment of interoperability, performance, and efficiency is necessary, requiring real-world network condition simulations.
With the introduction of KAI, AI providers, semiconductor manufacturers, and network equipment manufacturers can expedite their design and deployment processes by addressing performance issues preemptively. This innovation promises to significantly improve the reliability and efficiency of data center operations in the rapidly evolving AI landscape.
