Title of Document: APPLYING PERCEPTRONS TO SPECULATION IN COMPUTER ARCHITECTURE

Speculation plays an ever-increasing role in optimizing the execution of programs in computer architecture. Speculative decision-mak rs are typically required to have high speed and small size, thus limiting their complexity and capability. Because of these restrictions, predictors often consider only a small subset of the available data in making decisions, and consequently do not realize their potential accuracy. Perceptrons, or simple neural networks, can be highly useful in speculation for their ability to examine larger quantities of availabl dat, nd identify which data lead to accurate results. Recent research has demonstrated that perceptrons can operate successfully within the strict size and latency restrictions of speculation in computer architecture. This dissertation first studies how perceptrons can be made to predict accurately when they directly replace the traditional pattern table predictor. Several weight training methods and multiple-bit perceptron topologies are modeled and evaluated in their ability to learn data patterns th pattern tables can learn. The effects of interference between past data on perceptrons are evaluated, and differen