Merged Logic and Memory Fabrics for Accelerating Machine Learning Workloads

Designing hardware accelerators for machine learning (ML) applications is a well-researched problem. This article presents a tutorial regarding new computing architectures, circuits techniques, and multiple promising device technologies for in-memory computing targeting ML workloads.