Cache Replacement Scheme based on Back Propagation Neural Networks

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Presentation transcript:

Cache Replacement Scheme based on Back Propagation Neural Networks -Rakesh Ramananda

Aim of the Project To build a cache replacement Algorithm for a set associative memory using BPNN. Compare the test results with existing algorithms.

Basic info What is Cache? Why its performance place an important role? Existing Algorithm - LRU - MRU - FIFO Optimal replacement. Shadow Directory

Implementation details of BPNN Feature vector Memory Address being accessed for a 16 set 4 way set associative cache with line size=16 Input vector = TAG FIELD INDEX Number of neurons in input layer = size of tag +size of index Number of neurons in output layer = size of index. 10110111000 0110

Work till now Training of BPNN for 16 bit addresses for a 4 way 16 sets cache. number of hidden layers is 2 (sigmoidal function) size of hidden layers is varied from 2,4,8,10. for line size 8 and 16. Hit ratio currently is 57%

Future Work Train the network for various length of input data i.e. varying associativity, address width and no.of sets in the cache. Train network with different activation function. If time permits design a cache controller using fuzzy control and compare the functionality between the two.

Question?? 