The rapid advancement of Artificial Intelligence (AI) compute and cloud-native compute has profoundly reshaped the semiconductor industry, driving unprecedented demand for high-performance, energy-efficient, and scalable silicon chips capable of supporting massive parallel processing, ultra-low latency, and distributed workloads. Traditional design approaches, optimized for general-purpose computing, struggle to meet the demands of these new compute paradigms requiring a reimagined approach to silicon chip development that prioritizes resiliency, adaptability, reliability, and availability. This talk examines the profound impact of AI and cloud-native compute on semiconductor requirements, emphasizing the need for resilient and adaptable design techniques alongside advanced testing and validation methodologies. We will explore cutting-edge approaches to chip design, such as state-of-the art architectures, advanced process nodes, modular integration framework using AI-driven automation, fault-tolerant and dynamically adaptable circuits, and advanced packaging, which enhance chip robustness and flexibility to accommodate ever-evolving workloads. Concurrently, we will delve into sophisticated validation frameworks, including high-fidelity simulation and emulation environments, AI-driven fault-injection testing, accelerated stress testing, and in-situ performance monitoring, to ensure chips maintain exceptional performance under diverse operating conditions. By adopting these resilient design techniques and rigorous testing methodologies, the semiconductor industry can meet the stringent demands of AI and cloud-native systems, delivering chips that are not only high-performing and reliable but also adaptable to future technological advancements, thus enabling the next era of computational innovation.
