Advancing AI with Energy-Efficient Architectures: Innovations in Fab Process, Packaging, and System Integration

Energy-efficient AI architectures have become paramount for continued scaling of AI performance, necessitating innovative approaches that reduce energy per operation while improving performance. Prioritizing performance per Watt is essential for advancing AI, where energy efficiency translates into larger deployments, reduced cooling requirements, and faster, more efficient model training and inference generation. This level of optimization demands that the industry accelerate innovation in fab process technology, advanced packaging, system interconnect, and thermal solutions. More performant accelerators intrinsically improve datacenter-level efficiency with fewer nodes for a given performance level, reducing networking and system overhead. Leveraging new fab process optimizations, compute and memory chiplets enables delivery of more performance on a single package. Advanced packaging techniques integrate these components, enabling higher interconnect density, improved thermal management, and better overall performance. These innovations collectively address the growing needs of AI and high-performance computing, promising substantial improvements in energy efficiency, scalability, and integration.