The escalating demand for computational power, driven by AI, IoT, and autonomous systems, makes energy efficiency across the semiconductor value chain critical. A holistic, AI-driven approach is essential for designing, manufacturing, and integrating highly efficient chips, enabling sustainable silicon enabled, software defined, AI powered applications.
At design, advanced AI optimizes power consumption. Generative AI explores novel, energy-optimized architectures; Analytical AI identifies power hotspots; and Predictive AI anticipates characteristics, ensuring robust, efficient chips from inception.
In manufacturing, AI optimizes production and facility efficiency. Advanced analytics transform raw data to optimize processes, maximize yield, and reduce rework. AI-driven facility management, leveraging data and digital twins, optimizes large energy consumers like cooling infrastructures for continuous, real-time energy optimization, cost savings, and a reduced carbon footprint.
This commitment extends to the broader ecosystem. By enabling inherently energy-efficient chips, these solutions directly contribute to more sustainable, performant final applications—from data centers to electronics. Embedding generative, analytical, and predictive AI accelerates innovation, making next-generation semiconductors fundamentally more efficient for a sustainable digital future.
