Branch Prediction Techniques and Optimizations
$10-30 USD
Bezahlt bei Lieferung
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.
Projekt-ID: #9153205