Arman Kazemi graduated from Shahid Beheshti University, Tehran, Iran in 2017 with a bachelor's in computer engineering and started his PhD at Notre Dame immediately thereafter. His research interests include hardware/software co-design, low-power circuit design, and in-memory computing. He is particularly interested in inventions leveraging emerging CMOS-compatible technologies (e.g., ferroelectric materials). His research usually targets reducing computational resource requirements of machine learning applications, enabling their utilization at the edge.
Representative work: Arman Kazemi, Ramin Rajaei, Kai Ni, Suman Datta, Michael Niemier, X. Sharon Hu, “A Hybrid FeMFET-CMOS Analog Synapse Circuit for Neural Network Training and Inference,” to appear at IEEE International Symposium on Circuits and Systems, 2020.