Revisiting Global Value Prediction: A Resurgent Complement to Local Predictors

Value prediction is a microarchitectural technique that improves instruction-level parallelism by speculatively predicting the outcomes of instructions and eliminating data dependencies. While local value predictors have seen substantial progress by exploiting context-sensitive value locality, global value prediction remains relatively underexplored due to practical limitations such as inaccurate prediction, value delay constraint, and excessive hardware cost. This paper revisits global value prediction with a focus on addressing these long-standing challenges and presents EgDiff, a redesigned global predictor that introduces aggressive confidence mechanisms tailored for deep pipelines, performs non-speculative updates using committed values to preserve critical correlations, and employs deferred prediction to avoid premature decisions based on incomplete global history. To reduce hardware overhead, EgDiff uses a distance polling technique that compresses the predictor table by over 95% without sacrificing accuracy. Experimental results show that EgDiff achieves more than 99% accuracy and delivers a 4.37% average IPC speedup. When combined with EVES, a state-of-theart local predictor, a compact 19KB hybrid achieves a 6.16% speedup, already surpassing 32KB EVES alone (4.81%). With unlimited storage, the hybrid further reaches a $\mathbf{7. 0 2 \%}$ speedup. These findings demonstrate that global and local value prediction are complementary, and that revisiting global prediction with modern design principles offers practical and significant benefits in processor.