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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 =
10110111000
0110
TAG FIELD
INDEX
• Number of neurons in input layer = size of tag +size of index
• Number of neurons in output layer = size of index.
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?? 
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