Computational and Storage Power Optimizations for the O-GEHL Branch Predictor

In recent years, highly accurate branch predictors have been proposed primarily for high performance processors. Un-fortunately such predictors are extremely energy consuming and in some cases not practical as they come with excessive prediction latency. One example of such predictors is the O-GEHL predictor. To achieve high accuracy, O-GEHL re-lies on large tables and extensive computations and requires high energy and long prediction delay. In this work we propose power optimization techniques that aim at reducing both computational complexity and storage size for the O-GEHL predictor. We show that by eliminating unnecessary data from computations, we can re-duce both predictor’s energy consumption and delay. More-over, we apply information theory findings to remove re-dundant storage, without any significant accuracy penalty. We reduce the dynamic and static power dissipated in the computational parts of the predictor by up to 74 % and 65% respectively. Meantime we improve performance by up to 12 % as we make faster prediction possible