Mehdi Sadi

Mehdi Sadi is an Assistant Professor of Electrical and Computer Engineering at Auburn University's College of Engineering. He holds a Ph.D. from the University of Florida, an M.S. from the University of California-Riverside, and a B.S. from Bangladesh University of Engineering and Technology. His research focuses on secure and reliable system-on-chip design, AI/ML-driven VLSI CAD/EDA, neuromorphic hardware, and emerging post-CMOS computing technologies.

Notable achievements include earning the NSF CAREER Award for chiplet-based design optimization and a $175k NSF grant for magnetic RAM research. His work integrates machine learning with hardware co-design to enhance AI accelerators' performance, energy efficiency, and security. Recent projects include adversarial attack mitigation on AI hardware and reliability analysis of neuromorphic systems.

Dr. Sadi's contributions span chiplet architecture, memory systems (e.g., STT-MRAM/SOT-MRAM), and fault-tolerant computing. He actively publishes on topics like skyrmion logic gates and TRNG implementations using MRAM. His work bridges theoretical machine learning advancements with practical hardware implementations, addressing critical challenges in next-generation computing systems.