12 Facts About Branch predictor

The purpose of the branch predictor is to improve the flow in the instruction pipeline.

Branch predictor prediction is not the same as branch target prediction.

Two-Level Branch Predictor, referred to as Correlation-Based Branch Predictor, uses a two-dimensional table of counters, called "Pattern History Table".

Advantage of the two-level adaptive Branch predictor is that it can quickly learn to predict an arbitrary repetitive pattern.

Local branch predictor has a separate history buffer for each conditional jump instruction.

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An alloyed branch predictor combines the local and global prediction principles by concatenating local and global branch histories, possibly with some bits from the program counter as well.

An agree Branch predictor is a two-level adaptive Branch predictor with globally shared history buffer and pattern history table, and an additional local saturating counter.

Hybrid Branch predictor, called combined Branch predictor, implements more than one prediction mechanism.

Alpha 21264 and Alpha EV8 microprocessors used a fast single-cycle next-line predictor to handle the branch target recurrence and provide a simple and fast branch prediction.

The neural branch predictor research was developed much further by Daniel Jimenez.

In 2001, the first perceptron Branch predictor was presented that was feasible to implement in hardware.

Main advantage of the neural Branch predictor is its ability to exploit long histories while requiring only linear resource growth.