Applying Perceptrons to Speculation in Computer Architecture

Speculation plays an ever-increasing role in optimizing the execution of programs in computer architecture. Speculative decision-makers 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 available data, and 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 that pattern tables can learn. The effects of interference between past data on perceptrons are evaluated, and different interference reduction strategies are explored. Perceptrons are then applied to two speculative applications: data value prediction and dataflow critical path prediction. Several new perceptron value predictors are proposed that can consider longer or more varied data histories than existing table-based value predictors. These include a global-based local predictor that uses global correlations between data values to predict past local values, a global-based global predictor that uses global correlations to predict past global values, and a bitwise predictor that can use global correlations to generate new data values. Several new perceptron criticality predictors are proposed that use global correlations between instruction behaviors to accurately determine whether instructions lie on the critical path. These predictors are evaluated against local table-based approaches on a custom cycle-accurate processor simulator, and are shown on average to have both superior accuracy and higher instruction-per-cycle performance. Finally, the perceptron predictors are simulated using the different weight training approaches and multiple-bit topologies. It is shown that for these applications, perceptron topologies and training approaches must be selected that respond well to highly imbalanced and poorly correlated past data patterns

Analysis of back propagation and radial basis function neural networks for handover decisions in wireless communication

In mobile systems, handoff is a vital process, referring to a process of allocating an ongoing call from one BS to another BS. The handover technique is very important to maintain the Quality of service. Handover algorithms, based on neural networks, fuzzy logic etc. can be used for the same purpose to keep Quality of service as high as possible. In this paper, it is proposed that back propagation networks and radial basis functions may be used for taking handover decision in wireless communication networks. The performance of these classifiers is evaluated on the basis of neurons in hidden layer, training time and classification accuracy. The proposed approach shows that radial basis function neural network give better results for making handover decisions in wireless heterogeneous networks with classification accuracy of 90%

Speculative Data Distribution in Shared Memory Multiprocessors

This work explores the possibility of using speculation at the directories in a cache coherent non-uniform memory access multiprocessor architecture to improve performance by forwarding data to their destinations before requests are sent. It improves on previous consumer prediction techniques, showing how to construct a predictor that can handle a tradeoff of accuracy and coverage. This dissertation then explores the correct time to perform consumer prediction, and show how a directory protocol can incorporate such a scheme. The consumer prediction enhanced protocol that is developed is able to reduce the runtime of a set of scientific benchmarks by 10%-20%, without substantially reducing the runtime of other benchmarks; specifically, those benchmarks feature simple phased behavior and regularly distribute data to more than two processors. This work then explores the interaction of consumer prediction with two other forms of prediction, migratory prediction and last touch prediction. It demonstrates a mechanism by which migratory prediction can be implemented using only the storage elements already present in a consumer predictor. By combining this migratory predictor with a consumer predictor, it is possible to produce greater speedups than did either individually. Finally, the signatures of the last touch predictor can be applied to improve the performance of consumer prediction