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Branch Prediction Techniques and Optimizations

Need code and simulation results as follows

Branch prediction is one of the ancient performance improving techniques which

still finds relevance into modern architectures. While the simple prediction

techniques provide fast lookup and power efficiency they suffer from high mis-

prediction rate. On the other hand, complex branch predictions – either neural

based or variants of two-level branch prediction – provide better prediction

accuracy but consume more power and complexity increases exponentially. In

addition to this, in complex prediction techniques the time taken to predict the

branches is itself very high – ranging from 2 to 5 cycles – which is comparable to

the execution time of actual branches. Branch prediction is essentially an

optimization (minimization) problem where the emphasis is on to achieve lowest

possible miss rate, low power consumption and low complexity with minimum

resources. In this survey paper we review the traditional Two-level branch

prediction techniques; their variants and the underlying principles which make

them predict accurately. We also briefly discuss the Perceptron based technique

which uses lightweight neural network technique to predict the outcome of

branches.

スキル: 建築設計

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haihothi

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