Infrastructure AI

What Is Infrastructure AI?

Infrastructure AI, often known as the “AI stack,” encompasses the hardware and software systems essential for the development, deployment, and maintenance of artificial intelligence (AI) and machine learning (ML) applications. It includes vast AI software as a service (SaaS) offerings via laptops and phones, copilots, and ML frameworks, as well as information systems designed with AI servers, networking, and global cloud infrastructure to provide AI at scale.

Unlike conventional IT systems emphasizing standard computing and storage capabilities, infrastructure AI is explicitly designed for big data analytics and AI/ML applications. These configurations are optimized to handle large volumes of data, run complex algorithms, and empower ML models effectively. Infrastructure AI is a backbone for data analysis, predictive modeling, generative AI, agentic AI, and physical AI, fostering innovation across various sectors. Its key attributes—scalability, reliability, and efficiency, make infrastructure AI indispensable in numerous industries.

Why Is Infrastructure AI Important?

Many business and technology challenges require advanced AI solutions to enhance reliability and efficiency, which are crucial for organizations aiming to optimize operations and make quick decisions. The infrastructure AI helps optimize cutting-edge systems for improving the efficiency of various AI/ML workloads, including an emerging class of workloads, namely, serving large language models (LLMs). It gives the framework for all the necessary components to build and deploy applications rapidly.

Infrastructure AI is vital to handle complex AI tasks, as it provides the necessary computational power and offers many benefits, including:

  • Quick decision-making and accurate insights
  • Enhanced performance
  • Increased scalability and flexibility
  • Substantial cost optimization
  • More collaborations for developers
  • Regulatory compliance
  • Reliability

Sighting so many benefits, industries such as healthcare, finance, automotive, and precision medicine rely heavily on infrastructure AI.

Key Components of Infrastructure AI

Infrastructure AI comprises several vital components that facilitate the development and implementation of AI/ML-based applications. Each component plays a specific role to ensure smooth operation:

  • Data Workloads and Pre-Processing: AI's effectiveness hinges on the quality and volume of data. AI systems must handle large, diverse, and often unstructured datasets, necessitating robust solutions for data input, pre-processing, and storage to provide accessible training and inference data.
  • ML Models: ML models are the core of AI systems, identifying patterns and making predictions based on large datasets. ML models require substantial computing and storage capabilities to function effectively and power applications such as computer vision and natural language processing.
  • Compute Resources: Advanced AI activities demand high-performance hardware and advanced software resources.
    • Hardware Resources: Components like graphics processing units (GPUs), tensor processing units (TPUs), AI accelerators, and other specialized processors enhance data processing and model training, enabling efficient parallel computations for machine learning tasks.
    • Software Resources: Various tools and frameworks are essential for crafting, deploying, and managing AI applications. The development process often involves ML libraries like TensorFlow or PyTorch, programming languages like Python, and platforms for serving models, which streamline monitoring and version control.
  • Networking: Effective AI architecture requires a strong networking framework for data movement and component interaction, particularly in the cloud. High-speed networks facilitate distributed computing and real-time inference for applications deployed across various locations, enhancing AI systems' scalability, performance, and resilience.
  • Data Storage: Scalable solutions, such as cloud storage and data lakes, store vast datasets and ensure they’re easily accessible for AI model training and execution.

These components integrate seamlessly to create, deploy, and manage AI systems effectively at scale.

How Does Infrastructure AI Work?

Infrastructure AI combines hardware, software, and networking to create a unified platform for AI and ML applications. Here’s how these components work together:

  • Data Management: Large datasets are gathered from multiple sources, cleaned, and formatted to ensure they are structured and ready for processing.
  • Training and Predictions: GPUs and TPUs provide the necessary resources to train AI models by analyzing data and making predictions.
  • Model Deployment: After training and validation, models are deployed for specific applications, such as enhancing chatbots or performing predictive analytics.
  • Ongoing Monitoring: Continuous performance tracking is implemented to maintain system efficiency, allowing engineers to tweak algorithms or resources for sustained optimization.

Cadence for the Design of Infrastructure AI

The AI supercycle is evolving rapidly, with generative AI, agentic AI, and physical AI fueling an explosion in design activity for both training and inferencing. With its AI-driven chip-to-systems portfolio, Cadence is at the forefront of enabling AI development and adoption across various products.

Cadence provides silicon-proven Tensilica and Neo AI IP cores, advanced memory interfaces, and high-speed SerDes to reduce the time to market. We help customers choose the correct IP, subsystem, or silicon solution, addressing IP-to-SoC development challenges while minimizing risk. Cadence Integrity 3D-IC Platform is a “one-stop shop” solution that provides proven design flows for multi-chiplet design and advanced IC packaging. The Cadence.ai portfolio, with AI-driven optimization products and GenAI design agents powered by the Joint Enterprise Data and AI (JedAI) Platform and the AI-driven Verisium Verification Platform, is rapidly becoming integral to our customers' design flow.

The Cadence Reality Digital Twin platform helps transform the data centers for AI enhancements. This groundbreaking tool employs physics-based simulation driven by CFD to produce precise virtual representations of current or prospective data centers. Cadence’s computational fluid dynamics (CFD) software offers a clear framework for confidently validating and implementing liquid cooling in data centers.

The Cadence Cerebrus Intelligent Chip Explorer transforms the design of chips for data center infrastructure design through automated, ML-driven chip optimization. It streamlines the design flow, ensuring superior PPA outcomes for SoC design. Engineers can concurrently optimize multiple blocks, enhancing productivity and speeding up the process from RTL to GDS. Cadence Cerebrus scales efficiently with distributed computing and is adaptable for on-premises or cloud resources. Its intuitive interface offers interactive results analysis, empowering engineers for seamless control and accelerated innovation in chip design for data centers.

The Joint Enterprise Data and AI (JedAI) Platform accelerates the AI-based chip design. It improves productivity by allowing design teams to glean actionable intelligence from the massive amount of chip design data. Engineers can seamlessly manage both structured and non-structured data. The Cadence JedAI Platform makes it easier for designers to manage design complexities associated with emerging consumers, hyperscale computing, 5G communications, automotive and mobile applications, and more.

Virtuoso Studio's custom design solution provides innovative features, reimagined infrastructure for unrivaled productivity, and new levels of integration that stretch beyond classic design boundaries.

Verisium Verification Platform represents a generational shift from single-run, single-engine algorithms in EDA to algorithms that leverage big data and AI to optimize multiple runs of multiple engines across an entire SoC design and verification campaign. By deploying the Verisium platform, all verification data, including waveforms, coverage, reports, and log files, are brought together in the Cadence JedAI platform. ML models are built, and other proprietary metrics are mined from this data to enable a new tool class that dramatically improves verification productivity.

Allegro X Design Platform is the ultimate solution for navigating modern electronic complexities that help support your diverse PCB design needs. As a full-stack engineering platform, it provides a scalable and highly integrated environment for multi-board electronic system design.

Optimality Intelligent System Explorer accelerates the time to market; its multidisciplinary analysis and optimization (MDAO) capability helps achieve 10X productivity gains by exploring the full design space for optimal electrical design and can be used for Level 3+ automation of vehicles.