MODELING OF TRACE- AND BLOCK-BASED CACHES

2007 ◽  
Vol 16 (05) ◽  
pp. 711-729 ◽  
Author(s):  
AZAM BEG ◽  
YUL CHU
Keyword(s):  
Compiler Optimization ◽  
Network Models ◽  
Performance Comparison ◽  
Conventional Instruction ◽  
Neural Network Models ◽  
Linear Behavior ◽  
Block Based ◽  
Instruction Caches ◽  
Improve Instruction ◽  
Trace Cache

Recent cache schemes, such as trace cache, (fixed-sized) block cache, and variable-sized block cache, have helped improve instruction fetch bandwidth beyond the conventional instruction caches. Trace- and block-caches function by capturing the dynamic sequence of instructions. For industry standard benchmarks (e.g., SPEC2000), performance comparison of various configurations of these caches using simulations can take days or even weeks. In this paper, we demonstrate that neural network models can be time-efficient alternatives to the simulations. The models are able to predict the multi-variate and non-linear behavior of trace- and block-caches, in terms of trace miss rate and average trace length. The models can be potentially used in compiler optimization or in pedagogical settings.

A study on performance of MHDA in training MLPs

2019 ◽  
Vol 36 (6) ◽  
pp. 1820-1834 ◽  
Author(s):  
Sree Ranjini K.S.
Keyword(s):  
Network Models ◽  
Performance Comparison ◽  
Data Sets ◽  
Training Algorithms ◽  
Neural Network Models ◽  
Content Type ◽  
Dragonfly Algorithm ◽  
Deterministic Algorithms ◽  
Mlp Model ◽  
Optimal Set

Purpose In recent years, the application of metaheuristics in training neural network models has gained significance due to the drawbacks of deterministic algorithms. This paper aims to propose the use of a recently developed “memory based hybrid dragonfly algorithm” (MHDA) for training multi-layer perceptron (MLP) model by finding the optimal set of weight and biases. Design/methodology/approach The efficiency of MHDA in training MLPs is evaluated by applying it to classification and approximation benchmark data sets. Performance comparison between MHDA and other training algorithms is carried out and the significance of results is proved by statistical methods. The computational complexity of MHDA trained MLP is estimated. Findings Simulation result shows that MHDA can effectively find the near optimum set of weight and biases at a higher convergence rate when compared to other training algorithms. Originality/value This paper presents MHDA as an alternative optimization algorithm for training MLP. MHDA can effectively optimize set of weight and biases and can be a potential trainer for MLPs.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper
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