Building the Validation Backbone of AI Infrastructure

Senior System Test Engineer Kandiraja Narayanasamy works on validation of large-scale data-center hardware platforms at one of the world’s leading AI computing companies, where his work supports platforms designed to run modern AI workloads. Over more than a decade in semiconductor engineering, he has developed validation architectures, hardware modeling environments, and automation frameworks used to verify the reliability of complex computing platforms.

Prior to this role, Narayanasamy held multiple engineering positions at Intel focused on processor validation, hardware modeling, and system architecture testing. His work has included designing validation frameworks, building infrastructure for large-scale test environments, and collaborating with cross-functional engineering teams responsible for bringing new hardware platforms into production.

In this TechBullion interview, Narayanasamy discusses how validation engineering is evolving alongside AI infrastructure, the challenges of testing modern computing systems, and the development of his Hardware-Aware Validation Structure (HAVS) model for building more scalable and maintainable validation environments.

EW: Kandiraja, your career has focused heavily on system-level hardware validation across semiconductor and data-center platforms. What initially drew you to validation engineering as a specialization?

KN: I started my career working in systems engineering within a large semiconductor manufacturing environment, where I supported infrastructure connected to automated production systems and data-center platforms. For someone just out of university, working with highly automated environments and hundreds of interconnected machines was a steep learning curve.

As I worked more closely with those systems, I became increasingly curious about the underlying hardware architecture and how those systems were validated before deployment. That interest led me to join a hardware modeling team focused on developing models used for validation, and that role helped me build a deeper understanding of hardware architecture and system behavior.

What attracted me most was the opportunity to understand the full end-to-end lifecycle—from architecture and modeling through system validation. Over the past decade working in validation engineering, I have developed a strong connection to this field because it sits at the point where design intent meets real-world system behavior.

EW: Engineers often think about hardware innovation in terms of processor design or architecture, but validation plays an equally critical role before systems reach production. How would you describe the role of validation engineering in the lifecycle of modern computing platforms?

KN: Validation is fundamental to whether any product ultimately succeeds in the market. Across my experience, I have seen genuinely innovative designs fail to reach production simply because they could not pass validation.

In semiconductor systems especially, products must operate within complex multi-vendor environments. A processor or accelerator may interact with components, firmware, and software developed by many different organizations. In large computing systems and IoT environments, those interactions must remain stable across a wide range of configurations and operating conditions.

Validation engineering therefore serves two essential roles. First, it ensures that the system behaves correctly across all intended operating scenarios. Second, it provides the structured workflows needed to move products efficiently through qualification and into production. Without a well-designed validation strategy, even strong hardware designs can struggle to meet time-to-market expectations.

EW: AI infrastructure places unusual demands on hardware systems, particularly as accelerators and high-performance servers move rapidly from prototype to production. What validation challenges emerge when these systems are developed under compressed timelines?

KN: AI infrastructure introduces unique validation challenges because of the way modern AI workloads exercise hardware systems. Training and inference workloads can drive sustained utilization across many parts of a processor or accelerator at the same time, including compute units, memory subsystems, interconnects, and power management components. In practice, these workloads often push hardware platforms close to their operational limits.

Traditional validation environments across the industry have focused primarily on verifying interface correctness and functional behavior. That approach worked well for earlier generations of computing systems, where workloads did not consistently stress every subsystem simultaneously. With AI infrastructure, however, validation must also account for sustained performance, thermal behavior, and long-duration system stability under continuous high utilization.

Another challenge is that manufacturing and lab environments often need to adapt their infrastructure to support these new hardware platforms while development programs are already underway. Because of that, validation architecture needs to be designed early and executed efficiently in order to keep pace with accelerated hardware development cycles.

EW: In a recent article, you described limitations that can arise in traditional script-driven validation environments. What structural issues tend to develop as validation frameworks grow around collections of scripts?

KN: Script-driven validation environments tend to become increasingly complex as they evolve. Over time, large numbers of scripts accumulate to support different test scenarios, features, and hardware configurations.

The challenge arises when engineers need to enhance or refactor those scripts—for example, to improve error handling, enable modular testing, or isolate specific components for unit testing. As the number of interdependencies grows, even small changes can affect many parts of the codebase. Another complication is that orchestration frameworks often allow multiple programming languages. While that flexibility can be helpful initially, it can also introduce inconsistencies and feature conflicts between languages. As the environment scales, maintaining and extending the framework becomes significantly more difficult.

EW: You developed what you call the Hardware Abstraction-based Validation Suite (HAVS) model as an alternative framework architecture. What motivated the development of that approach, and what problems were you aiming to solve?

KN: As semiconductor platforms evolve, many new products are derived from earlier architectures. Because of that, code reuse becomes very important in validation environments.

In traditional script-based validation frameworks, however, reusing an existing codebase across products can be unpredictable. Sometimes scripts can be adapted with minor changes, while in other cases they require extensive modification because hardware dependencies are embedded directly in the test logic.

The HAVS model grew out of my effort to address this problem by introducing a structured Hardware Abstraction Layer (HAL) at the core of the validation framework. In this approach, the HAL provides a stable interface for interacting with hardware components, while product-specific functionality is implemented through separate wrappers or configuration layers. This separation allows developers to clearly distinguish between reusable validation infrastructure and product-specific implementation details.

My motivation for this structure was influenced by earlier work developing HAL-based simulation models for firmware validation in pre-silicon environments. That experience showed how abstraction can significantly improve maintainability and reuse. Applying similar principles to validation environments makes it easier to scale testing frameworks across multiple hardware generations.

EW: A key aspect of the HAVS model involves representing hardware components through software abstraction layers. How does this architectural approach change the way validation teams develop and maintain test environments?

KN:  In the HAVS model, validation logic is implemented as methods associated with specific hardware component classes. This structure keeps validation engineers closely aligned with the hardware features they are testing.

When a new test needs to be added, the engineer naturally returns to the relevant hardware specifications for that component and implements the validation logic within the appropriate class. This reinforces awareness of how the hardware actually operates.

In contrast, script-based environments can sometimes obscure these relationships, because the validation logic becomes separated from the hardware abstraction it is testing.

EW: One of the persistent challenges in hardware validation is that validation work often begins before large volumes of hardware are available. How can abstraction-based validation architectures help teams continue development even when physical systems remain limited?

KN: One discipline I incorporated into the HAVS implementation is structuring every test into two sections:

1. Hardware-in-the-Loop (HIL)
2. Hardware-out-of-the-Loop (HOL)

The HOL portion allows developers to implement and test much of the validation logic even when physical hardware is not available. This is particularly valuable early in the development cycle when systems are limited.

For larger engineering teams, this structure also supports nightly regression testing. HOL tests can run continuously without requiring hardware resources, creating a regularly validated snapshot of the codebase and improving overall development stability.

EW: Large-scale AI infrastructure systems evolve quickly across successive hardware generations. What design principles allow validation frameworks to remain adaptable as processor architectures, accelerators, and interconnect technologies change?

KN:  Adaptability in validation frameworks begins with a strong understanding of system architecture. Although validation occurs later in the development lifecycle, engineers benefit greatly from participating in design discussions and understanding the architectural decisions behind the hardware.

When validation engineers engage early with design teams, they can anticipate which components will remain consistent across product generations and which parameters may change. Certain elements are common to nearly all hardware platforms—for example processors, system-on-chip components, and industry-standard communication protocols. By designing validation frameworks around these stable elements while parameterizing generational variables such as bandwidth or speed, teams can create architectures that scale across multiple hardware generations.

EW: Validation engineering requires close coordination with architecture, manufacturing, and product design teams. Can you share your best practices to help maintain effective collaboration across these groups during the validation and production ramp phases?

KN: Effective collaboration begins with participating in the product lifecycle from the earliest stages, including product kickoff meetings. That involvement helps validation teams understand design goals and potential risk areas.

During system bring-up phases, I try to align closely with engineering lab schedules and collect detailed information about failures or anomalies as they appear, because that feedback is valuable for improving validation coverage. I believe it is equally important to stay aware of the capabilities and constraints of manufacturing facilities, since production infrastructure may need to adapt for new hardware platforms. Clear documentation—such as dashboards, reports, and shared tracking tools—also plays a major role in keeping cross-organizational teams aligned.

Finally, collaboration often improves when engineers understand the workflows used by other teams, even if those processes are not formally documented. Building relationships with experts across architecture, manufacturing, and product engineering often leads to faster problem solving and better validation outcomes.

EW: Throughout your work on validation frameworks and system testing, you’ve contributed to environments where multiple engineering teams depend on shared validation infrastructure. How do you approach mentoring younger engineers or helping new team members learn the practices required to build reliable validation systems?

KN: Bringing new engineers into a validation team is not simply about increasing resources, I believe it is about helping them understand how their work contributes to the broader system.

I typically guide new team members through several stages:

1) First, understand the overall system architecture and where validation fits within the industry.

2) Learn the tools and infrastructure used by the validation team.

3) Become familiar with field and manufacturing requirements.

4) Gain confidence in executing tests and debugging failures.

5) Begin contributing through modular improvements to the codebase.

6) Eventually propose and prototype new ideas for improving validation workflows.

With the right mentoring structure, motivated engineers can quickly begin contributing meaningful improvements to the validation environment.

EW: As AI computing platforms continue to scale across data centers worldwide, how do you expect validation engineering practices to evolve in order to support increasingly complex hardware ecosystems?

KN: AI will increasingly play a role in scaling validation engineering itself. As hardware platforms grow more complex, validation environments must handle larger test matrices, longer execution cycles, and broader system interactions. Automation and AI-assisted tooling can help engineers manage that scale more effectively.

One advantage in hardware validation is that the underlying system behavior is well documented through architecture specifications and design models. That makes it possible to automate many parts of the validation workflow, from generating test scenarios to analyzing large volumes of system data.

In practical terms, AI will not replace the engineering discipline behind validation. Instead, it will allow engineers to focus more on designing meaningful test strategies while automated systems handle repetitive infrastructure tasks. Over time, this shift will allow validation teams to run deeper and longer test cycles, explore a wider range of operating conditions, and ultimately improve the reliability of complex computing platforms.