Mower is a micro-architecture technique which targets the branch misprediction penalty in superscalar processors. It speeds-up the misprediction recovery process by dynamically evicting stale instructions and correcting the Register Alias Table (RAT) using explicit control dependency tracking. Tracking control 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 recovery mechanism using a reorder buffer and a walker. The simplicity of the mechanism should permit easy implementation of Mower in an actual processor.

LaZy Superscalar is a processor architecture which delays the execution of fetched instructions until their results are needed by other instructions. This approach eliminates dead instructions and provides the necessary means to fuse dependent instructions across multiple control dependencies by explicitly tracking control and data dependencies through a matrix based scheduler. We present this novel redesign of scheduling, recovery and commit mechanisms and evaluate the performance of the proposed architecture. Our simulations using Spec 2006 benchmark suite indicate that LaZy Superscalar can achieve significant speed-ups while providing respectable power savings compared to a conventional superscalar processor.

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LaZy Superscalar is a processor architecture which delays the execution of fetched instructions until their results are needed by other instructions. This approach eliminates dead instructions and provides the necessary means to fuse dependent instructions across multiple control dependencies by explicitly tracking control and data dependencies through a matrix based scheduler. We present this novel redesign of scheduling, recovery and commit mechanisms and evaluate the performance of the proposed architecture. Our simulations using Spec 2006 benchmark suite indicate that LaZy Superscalar can achieve significant speed-ups while providing respectable power savings compared to a conventional superscalar processor.

Digital image storage and retrieval is gaining more popularity due to the rapidly advancing technology and the large number of vital applications, in addition to flexibility in managing personal collections of images. Traditional approaches employ keyword based indexing which is not very effective. Content based methods are more attractive though challenging and require considerable effort for automated feature extraction. In this chapter, we present a hybrid method for extracting features from images using a combination of already established methods, allowing them to be compared to a given input image as seen in other query-by-example methods. First, the image features are calculated using Edge Orientation Autocorrelograms and Color Correlograms. Then, distances of the images to the original image will be calculated using the L1 distance feature separately for both features. The distance sets will then be merged according to a weight supplied by the user. The reported test results demonstrate the applicability and effectiveness of the proposed approach.