Cost effective mixed-type value predictor using distributed classification method

We have investigated the conventional mixed-type value predictors, pointing out their limitations due to the inefficient use of data table entries. To improve the cost effectiveness of the conventional mixed-type value predictors in terms of the performance/cost ratio, we propose a new mixed-type value predictor, which uses the distributed classification method. The proposed value predictor has no centralized classification tables, but it uses distributed and local classification tables for each subsidiary predictor to classify instructions, to update data tables, and to predict result values. Static analysis of the cost reveals that the proposed value predictor decreases the cost by 30 and 10% compared with two conventional predictors, respectively. As well, the proposed value predictor increases the performance by 1% in terms of IPC, and, finally, improves the performance/cost ratio by 40 and 10% compared with two conventional methods.