Date of Award
Master of Science in Computer Science (MS)
College, School or Department Name
Department of Computer Science
Mower is a micro-architecture technique which targets branch misprediction penalties in superscalar processors. It speeds-up the misprediction recovery process by dynamically evicting stale instructions and fixing the RAT (Register Alias Table) using explicit branch dependency tracking. Tracking branch dependencies is accomplished by using simple bit matrices. This low-overhead technique allows overlapping of the recovery process with instruction fetching, renaming and scheduling from the correct path. Our evaluation of the mechanism indicates that it yields performance very close to ideal recovery and provides up to 5% speed-up and 2% reduction in power consumption compared to a traditional recovery mechanism using a reorder buffer and a walker. The simplicity of the mechanism should permit easy implementation of Mower in an actual processor.
Jin, Zhaoxiang, "MOWER : A NEW DESIGN FOR NON-BLOCKING MISPREDICTION RECOVERY", Master's Thesis, Michigan Technological University, 2015.