ML models' unique demands are driving innovations in hardware utilization and distributed computing. To maximize hardware efficiency, developers are creating specialized compilers, languages, and runtime systems tailored for ML workloads. These tools aim to fully leverage available computational resources, from CPUs to GPUs and specialized AI accelerators.
This repository aims to explore the newest ideas and optimization methods for machine learning systems.