Machine learning for embedded systems, edge computing, and Internet of Things (IoT) computing; hardware/software codesign for machine learning; model compression and optimization; latency and memory optimizations; data reuse and sharing techniques; applications of machine learning in embedded applications, including computer vision, natural language processing, speech processing, video analysis, and anomaly detection; hardware accelerators for deep learning; ethics in machine learning.
Prerequisite
CS 470 (Computer Architecture) with a C or better or ECE 452 (Computer Organization and Architecture) with a C or better.
