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An agree predictor is a two-level adaptive predictor with globally shared history buffer and pattern history table, and an additional local saturating counter. The outputs of the local and the global predictors are XORed with each other to give the final prediction.
The local predictor is a two-level table which records the history of individual branches. It consists of a 1,024-entry by 10-bit branch history table. A two-level table was used as the prediction accuracy is similar to that of a larger single-level table while requiring fewer bits of storage. It has a 1,024-entry branch prediction table.
The general rule for a two-level adaptive predictor with an n-bit history is that it can predict any repetitive sequence with any period if all n-bit sub-sequences are different.[8] The advantage of the two-level adaptive predictor is that it can quickly learn to predict an arbitrary repetitive pattern.
This approach is employed in a variety of areas, including branch prediction in pipelined processors, value prediction for exploiting value locality, prefetching memory and files, and optimistic concurrency control in database systems. [1] [2] [3] Speculative multithreading is a special case of speculative execution.
Predictive engineering analytics (PEA) is a development approach for the manufacturing industry that helps with the design of complex products (for example, products that include smart systems). It concerns the introduction of new software tools, the integration between those, and a refinement of simulation and testing processes to improve ...
When computing a t-test, it is important to keep in mind the degrees of freedom, which will depend on the level of the predictor (e.g., level 1 predictor or level 2 predictor). [5] For a level 1 predictor, the degrees of freedom are based on the number of level 1 predictors, the number of groups and the number of individual observations. For a ...
Elimination of unnecessary branch instructions can make the execution of necessary branches, such as those that make up loops, faster by lessening the load on branch prediction mechanisms. Elimination of the cost of a branch misprediction which can be high on deeply pipelined architectures.
Data-driven prognostics usually use pattern recognition and machine learning techniques to detect changes in system states. [3] The classical data-driven methods for nonlinear system prediction include the use of stochastic models such as the autoregressive (AR) model, the threshold AR model, the bilinear model, the projection pursuit, the multivariate adaptive regression splines, and the ...