H.-S. Philip Wong

Willard R. and Inez Kerr Bell Professor in the School of Engineering, and Professor of Electrical Engineering

Stanford University

Artificial Intelligence Hardware

Hub Project Awards

• Artificial Intelligence (AI) Hardware ($6.7M)
  • Energy-Efficient and Scalable AI Hardware Systems through Heterogeneous Integration of Specialized Chiplets
    • This project will use innovations in semiconductor materials, integration technologies and AI system architecture to drastically improve energy consumption and performance of AI hardware. Interconnected heterogeneous chiplets, built using leading-edge CMOS and 3D CMOS+X semiconductor technologies such as carbon nanotube transistors, resistive memory, and oxide semiconductors, form the foundation for such AI systems.
• Artificial Intelligence (AI) Hardware ($5.7M)
  • Energy-Efficient, Scalable, and Self-Learning AI Hardware with 3D Electronic-Photonic-Integrated-Circuits
    • This project pursues transformative improvements in AI Hardware’s speed, energy-efficiency, scalability, and self-learning capabilities for next-generation U.S. defense needs. The project approach combines “best-of-both-worlds” innovations in photonics and electronics including CMOS+X devices and integrates them into compact 3D photonic-electronic-integrated circuit modules.
• Artificial Intelligence (AI) Hardware ($4M)
  • CMOS+X: Integrated Ferroelectric Technologies for Ultra Efficient AI Hardware
    • This project aims to substantially improve energy efficiency for future AI hardware by exploiting unique properties of ferroelectric materials. This project will focus on lowering the power supply voltage of computing hardware as well as achieving non-volatile memory that can be directly integrated with the microprocessor.

Hub Name Member List