Test Solutions for Enhanced AI Performance

We deliver the most extensive range of solutions to test AI infrastructure

End to End Support for Artificial Intelligence Development and Deployment

For over two decades, Teledyne LeCroy has played a key role in the reliable operation of technologies that enable Artificial Intelligence in Data Centers. Our test solutions are used throughout the entire ecosystem for high-performance Computing and Analytics, for Networks that allow for efficient moving and accessing of data, and for Storage Devices that are the backbone for Hot and Cold Storage in the cloud. We do this by delivering leading solutions for technologies like PCI Express, CXL, NVMe, Gigabit Ethernet, and SAS used in hyperscale environments to design and test engineers, from early adopters to system integrators.

    Artificial Intelligence - Interconnects

    Compute

    AI applications require High-Performance Computing in Data Centers to analyze vast amounts of data with high throughput and low latency, which drive modern computer and data-centric architectures.
    Artificial Intelligence - Networks

    Networks

    Moving large amounts of data within racks, Data Centers and campuses accelerates the pursuit of faster and more efficient network technologies.
    Artificial Intelligence - Storage

    Storage

    The ever-increasing demand for storage capacity and the quest to access data from everywhere drive the evolution of cloud and hybrid storage solutions, as well as storage interface technologies.

    Compute - Interconnects, Processing, Data Flow and Memory Management

    At the heart of AI’s transformative power are the Computing and processing requirements that make it all possible. AI Workloads drive the transformation of High-Performance Computing (HPC) in Data Centers to deliver trillions of calculations per second enabling image recognition, natural language comprehension, and trend predictions with astonishing speed and accuracy. The parallel processing systems enable AI to multitask with efficiency, mirroring the complexity of the human brain.

    Colorful image of an AI brain representing PCIe / CXL
    Colorful image of an AI brain representing PCIe / CXL

    Teledyne LeCroy Summit analyzers, exercisers, jammers, interposers, and test systems help build and optimize the fastest and latest systems using PCIe to support AI. These devices and computing systems use the high-speed interface that connects AI accelerators, such as GPUs and custom silicon chips, to the central processing unit (CPU). Its continuous evolution ensures that AI systems remain at the cutting edge of technology, ready to meet the challenges of tomorrow’s data-driven world.

    • Scalability: With each new generation, PCIe doubles its bandwidth, accommodating the growing demands of AI applications. The latest PCIe 6.0 specification offers a data transfer rate of 64 GT/s per pin, ensuring that AI systems can handle increasingly complex tasks.
    • Data Security: PCIe includes robust security features to protect data integrity and confidentiality. For example, PCIe 6.0 and later versions incorporate Integrity and Data Encryption (IDE) modules that safeguard data transmitted over PCIe links. These features ensure that sensitive AI data remains secure from cyber threats.
    • Versatility: PCIe is used in various form factors, from large chips for deep-learning systems to smaller, spatial accelerators that can be scaled up to process extensive neural networks requiring hundreds of petaFLOPS of processing power.
    • Energy Efficiency: Newer PCIe versions introduce low-power states, contributing to greater power efficiency in AI systems. This is essential for sustainable and cost-effective AI operations.
    • Interconnectivity: PCIe facilitates the interconnection of computing, accelerators, networking, and storage devices within AI infrastructure, enabling efficient data center solutions with lower power consumption and maximum reach.

    CXL holds significant promise in shaping the landscape of AI and Teledyne LeCroy solutions are the only way to test and optimize today’s CXL systems. Memory efficiency, latency reduction, and performance are all achieved using Teledyne LeCroy solutions supporting CXL testing and compliance - all crucial for maintaining low latency and high throughput. This is especially important for bandwidth-intensive AI workloads that require quick access to large datasets.

    • Memory Capacity Expansion: CXL allows connecting a large memory pool to multiple processors or accelerators. This is crucial for AI/HPC applications dealing with massive datasets.
    • Data Security: CXL includes robust security features to protect data integrity and confidentiality. For example, CXL 2.0 and later versions incorporate Integrity and Data Encryption (IDE) modules that safeguard data transmitted over CXL links. These features ensure that sensitive AI data remains secure from cyber threats.
    • Reduced Latency: CXL’s low-latency design ensures data travels quickly between computing elements. AI/ML workloads benefit from minimized wait times.
    • Interoperability: CXL promotes vendor-neutral compatibility, allowing different accelerators and memory modules to work seamlessly together.
    • Enhanced Memory Bandwidth: CXL significantly improves memory bandwidth, ensuring data-intensive workloads access data without bottlenecks.

    Networks - High Speed Ethernet, Data Throughput, Fabrics and Networks

    Recent Large Language Models, like GPT-4 require hundreds of millions and more of parameters that are delivered from disparate sources through scalable networks. For this, high-speed networks and networking technologies must support low latency and efficient transfer of information optimized to these new workloads.

    Wired connections to AI infrastructures
    Stylized worldwide networks for AI back-end testing

    Gigabit Ethernet, operating at 1 Gbps (gigabit per second), provides rapid data transfer rates. This speed is crucial for handling large datasets in AI workloads. Terabit Ethernet, operating at 1 Tbps (terabit per second), facilitates the seamless exchange of massive datasets. It supports emerging technologies like the Internet of Things (IoT), artificial intelligence (AI), and big data analytics.

    • Real-Time Responsiveness: Low latency is essential for AI systems. Gigabit Ethernet minimizes delays, ensuring timely interactions between components like GPUs, CPUs, and Storage Devices.
    • Real-Time Decision-Making: Terabit Ethernet enables real-time AI-driven decision-making. Its high bandwidth ensures efficient communication between AI nodes.
    • Lossless Networking: Traditional Ethernet may drop packets during congestion, affecting AI model accuracy. However, emerging technologies promise “lossless” transmission, ensuring data integrity even under heavy loads
    • Scalability: As AI models grow in complexity, scalable infrastructure becomes vital. Gigabit Ethernet allows seamless expansion by connecting additional servers and devices. Terabit Ethernet accommodates their exponential growth, ensuring efficient connectivity and data exchange

    Teledyne LeCroy XENA products enable companies to optimize and future-proof their AI back-end network fabric to handle massive amounts of time-critical traffic. Data Center architectures for AI workloads often adopt a spine-and-leaf structure, connecting thousands of AI accelerators and storage solutions through low-latency L2/L3 networking infrastructure with 400 to 800 Gbps port speeds. RDMA over Converged Ethernet (RoCE) is a promising choice for a storage data transport protocols.

    How to test Data Center Switches Optimized for Artificial Intelligence - white paper

    • Data Center Bridging (DCB): facilitate high-throughput, low-latency, and zero packet loss transport of RDMA packets (lossless traffic) alongside regular best-effort traffic (lossy traffic).
    • Priority Flow Control (PFC): to prevent packet loss by prompting a sender to temporarily pause sending packets when a buffer becomes filled beyond a certain threshold.
    • Congestion Notification (CN): RoCEv1 and RoCEv2 implement a signaling between network devices that can be used to reduce congestion spreading in lossless Networks as well as decreasing latency and improving burst tolerance.
    • Enhanced Traffic Selection (ETS): enabling the allocation of a minimum guaranteed bandwidth to each Class of Service (CoS).

    Storage - SSDs, Datacenters, Data Management

    AI storage solutions must adapt quickly to scaling requirements of AI/ML workloads. That scalability of storage capacity and performance should be supported without disrupting ongoing operations and prevent overprovisioning and underutilization, all while supporting structured and unstructured data. At the core of storage infrastructure are technologies like, NVMe, SAS, and CXL used with Solid-State Drives, rotational media and high bandwidth memory elements.

    AI and Oakgate SSD Device Testing
    Colorful image of a head managing memories and AI
    Colorful image of AI head and SAS boxes

    The advent of AI and Machine Learning (ML) will only enhance the critical need for comprehensive solid-state storage device (SSD) testing. AI is expected to increase the demand for SSDs in Data Centers due to the high computational requirements of AI workloads. AI applications generate and process vast amounts of data, necessitating storage solutions with high-speed data access and processing capabilities.

    • Faster Data Access and Processing Speeds: essential for handling the large datasets and complex algorithms used in AI tasks. AI applications often involve frequent read and write operations, making SSDs more suitable than traditional HDDs for their performance and durability. This demand is likely to drive innovation in SSD technology and other high-performance storage solutions.
    • Specialized and Diverse Workloads: there will likely be a demand for storage solutions tailored specifically to the requirements of AI applications. This could include storage systems optimized for deep learning algorithms, real-time Analytics, or large-scale data processing.
    • Optimize Storage Systems: for efficiency, reliability, and performance. This involves using machine learning algorithms to predict storage usage patterns, automate data tiering, or improve data compression techniques.

    Teledyne LeCroy OakGate solutions provide testing capabilities for emerging CXL (Compute Express Link) memory devices that are poised to revolutionize Data Centers, especially for AI and machine learning workloads. AI platforms using CXL require high-speed, coherent memory access between CPUs and accelerators like GPUs, FPGAs, and TPUs, CXL memory devices will significantly enhance data transfer speeds, reduce latency, and improve overall system performance.

    • Functional and Performance Validation Testing: ensuring that new CXL devices perform per the standard when released to the market.
    • Quality and Compliance Testing: This means faster training and inference times for AI models, ultimately leading to more efficient and powerful machine learning operations in Data Centers.
    • Training and Inference Times: Testing AI systems for more efficient and powerful machine learning operations in Data Centers and increased coherent memory access between different processing units facilitates more complex and sophisticated AI algorithms and workflows.

    Testing Serial Attached SCSI (SAS) is crucial for supporting AI applications, particularly in terms of data storage and retrieval. By ensuring that SAS systems are thoroughly tested and compliant, AI applications can benefit from reliable, high-speed, and scalable data storage solutions, which are fundamental for effective AI operations.

    • High-Speed Data Transfer: SAS provides high-speed data transfer rates, which are essential for AI applications that require quick access to large datasets. This ensures that AI models can be trained and deployed efficiently.
    • Reliability and Redundancy: SAS systems are known for their reliability and redundancy features. This is important for AI, as it ensures that data is consistently available and protected against failures.
    • Scalability: SAS supports scalable storage solutions, allowing AI systems to grow and handle increasing amounts of data without compromising performance.
    • Compatibility: SAS is compatible with various Storage Devices and interfaces, making it versatile for different AI applications and environments.
    • Compliance Testing: Compliance testing for SAS ensures that the hardware meets industry standards for performance and reliability. This is critical for maintaining the integrity of AI systems that rely on these storage solutions

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