scholarly journals Request Schedule Oriented Compression Cache Memory

2018 ◽  
Vol 7 (2.19) ◽  
pp. 80
Author(s):  
G D.Kesavan ◽  
P N.Karthikayan
Keyword(s):  
Data Use ◽  
Optimization Techniques ◽  
Memory Access ◽  
Cache Memory ◽  
Optimization Process ◽  
Access Time ◽  
Cache Optimization ◽  
Speed Up ◽  
Compressed Storage ◽  
Cache Organization

Using cache memory the overall memory access time to fetch data gets reduced. As use of cache memory is related to a    system's performance, the caching process should take less time. To speed up caching process, there are many cache optimization techniques available. Some of the cache optimization process are Reducing Miss Rate, Reducing Miss Penalty, Re-ducing the time to hit in the cache etc. Re-cent advancement paved way for compressing data in cache, accessing recent data use pat-tern etc. All the techniques focus on increasing cache capacity or replacement policies in cache resulting in more hit ratio. There are many cache related compression and optimization techniques available which address only capacity and replacement related              optimization and their related issues. This paper deals with scheduling the requests of cache memory as per compressed cache organization. So that cache searching and indexing speed gets reduced considerably and service the request in a faster manner. For capacity and replacement improvements Dictionary sharing based caching is used. Through this scheme multiple requests are foreseen using pre-fetcher and are searched as per cache organization, promoting easier indexing process.The benefit comes from both compressed storage and also easier storage ac-cess. 

scholarly journals Pre-Emphasis Pulse Design for Random-Access Memory

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1454
Author(s):  
Yoshihiro Sugiura ◽  
Toru Tanzawa
Keyword(s):  
Time Constant ◽  
Random Access ◽  
Memory Cell ◽  
Random Access Memory ◽  
Memory Access ◽  
Access Time ◽  
Access Memory ◽  
Delay Times ◽  
Cell Current ◽  
The Impact

This paper describes how one can reduce the memory access time with pre-emphasis (PE) pulses even in non-volatile random-access memory. Optimum PE pulse widths and resultant minimum word-line (WL) delay times are investigated as a function of column address. The impact of the process variation in the time constant of WL, the cell current, and the resistance of deciding path on optimum PE pulses are discussed. Optimum PE pulse widths and resultant minimum WL delay times are modeled with fitting curves as a function of column address of the accessed memory cell, which provides designers with the ability to set the optimum timing for WL and BL (bit-line) operations, reducing average memory access time.

scholarly journals Decision Tree-Based Adaptive Reconfigurable Cache Scheme

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 176
Author(s):  
Wei Zhu ◽  
Xiaoyang Zeng
Keyword(s):  
Decision Tree ◽  
Adaptive Algorithms ◽  
Memory Access ◽  
Access Time ◽  
Decision Tree Algorithm ◽  
Verilog Hdl ◽  
Tree Model ◽  
Cache Associativity ◽  
Cache Scheme ◽  
Reconfigurable Cache

Applications have different preferences for caches, sometimes even within the different running phases. Caches with fixed parameters may compromise the performance of a system. To solve this problem, we propose a real-time adaptive reconfigurable cache based on the decision tree algorithm, which can optimize the average memory access time of cache without modifying the cache coherent protocol. By monitoring the application running state, the cache associativity is periodically tuned to the optimal cache associativity, which is determined by the decision tree model. This paper implements the proposed decision tree-based adaptive reconfigurable cache in the GEM5 simulator and designs the key modules using Verilog HDL. The simulation results show that the proposed decision tree-based adaptive reconfigurable cache reduces the average memory access time compared with other adaptive algorithms.

Continuous Approximation Model for Hybrid Flexible Transit Systems with Low Demand Density

2021 ◽  
pp. 036119812199713
Author(s):  
Charalampos Sipetas ◽  
Eric J. Gonzales
Keyword(s):  
Agency Cost ◽  
Optimization Techniques ◽  
Optimization Process ◽  
Analytical Models ◽  
Continuous Approximation ◽  
Flexible Systems ◽  
Transit System ◽  
Transit Systems ◽  
Approximation Techniques ◽  
Service Areas

Flexible transit systems are a way to address challenges associated with conventional fixed route and fully demand responsive systems. Existing studies indicate that such systems are often planned and designed without established guidelines, and optimization techniques are rarely implemented on actual flexible systems. This study presents a hybrid transit system where the degree of flexibility can vary from a fixed route service (with no flexibility) to a fully flexible transit system. Such a system is expected to be beneficial in areas where the best transit solution lies between the fixed route and fully flexible systems. Continuous approximation techniques are implemented to model and optimize the stop spacing on a fixed route corridor, as well as the boundaries of the flexible region in a corridor. Both user and agency costs are considered in the optimization process. A numerical analysis compares various service areas and demand densities using input variables with magnitudes similar to those of real-world case studies. Sensitivity analysis is performed for service headway, percent of demand served curb-to-curb, and user and agency cost weights in the optimization process. The analytical models are evaluated through simulations. The hybrid system proposed here achieves estimated user benefits of up to 35% when compared with fixed route systems, under different case scenarios. Flexible systems are particularly beneficial for serving corridors with low or uncertain demand. This provides value for corridors with low demand density as well as communities in which transit ridership has dropped significantly because of the COVID-19 pandemic.

Optimization of the Dynamic Response of Semi-Submersibles: Influence of the Mooring System

2019 ◽  
Author(s):  
Shengtao Zhou ◽  
Frank Lemmer ◽  
Wei Yu ◽  
Po Wen Cheng ◽  
Chao Li ◽  
...  
Keyword(s):  
Life Cycle Cost ◽  
Design Space ◽  
Optimization Techniques ◽  
Optimization Process ◽  
Manufacturing Cost ◽  
Mooring System ◽  
Mooring Lines ◽  
Nsga Ii ◽  
Simulation Code ◽  
Set Up

Abstract The design and manufacturing cost of substructures is a major component of the total expenditure for a floating wind project. Applying optimization techniques to hull shape designs has become an effective way to reduce the life-cycle cost of a floating wind system. The mooring system is regarded as the component with the highest risk, mainly due to the poor accessibility. This paper extends the previous work by investigating the influences of the mooring design on the optimization process of a semisubmersible substructure. Two optimization loops are set up. In the first loop, only the main dimensions of a semi-submersible platform are parameterized without considering mooring lines (keep a constant mooring design). Nevertheless, the second loop introduces additional variables of the mooring lines. The objective is to minimize the tower-top displacement, fairlead fatigue damage, which are calculated by the in-house nonlinear dynamic simulation code SLOW, and the manufacturing cost of platform and mooring lines. The multi-objective optimization algorithm NSGA-II is employed to search for the optimal designs within the defined design space. The design space and the Pareto fronts are compared between the two optimizations. It is found that, although the mooring design does not have a significant impact on the platform design space, one obtains a different optimal set (Pareto front) if the mooring design and mooring loads are introduced into the platform optimization process. The results of this study are expected to give a better understanding in the relationship between platform and mooring design and serve as a basis for the optimization process of semi-submersible floating wind turbines.

Mixture Surrogate Models for Multi- Objective Optimization

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Shailesh S. Kadre ◽  
Vipin K. Tripathi
Keyword(s):  
Finite Element ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Optimization Techniques ◽  
Optimization Process ◽  
Multiple Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Costly Function ◽  
Selection Of

Multi-objective optimization problems (MOOP) involve minimization of more than one objective functions and all of them are to be simultaneously minimized. The solution of these problems involves a large number of iterations. The multi- objective optimization problems related structural optimization of complex engineering structures is usually solved with finite element analysis (FEA). The solution time required to solve these FEA based solutions are very high. So surrogate models or meta- models are used to approximate the finite element solution during the optimization process. These surrogate assisted multi- objective optimization techniques are very commonly used in the current literature. These optimization techniques use evolutionary algorithm and it is very difficult to guarantee the convergence of the final solution, especially in the cases where the budget of costly function evaluations is low. In such cases, it is required to increase the efficiency of surrogate models in terms of accuracy and total efforts required to find the final solutions.In this paper, an advanced surrogate assisted multi- objective optimization algorithm (ASMO) is developed. This algorithm can handle linear, equality and non- linear constraints and can be applied to both benchmark and engineering application problems. This algorithm does not require any prior knowledge for the selection of surrogate models. During the optimization process, best single and mixture surrogate models are automatically selected. The advanced surrogate models are created by MATSuMoTo, the MATLAB based tool box. These mixture models are built by Dempster- Shafer theory (DST). This theory has a capacity to handle multiple model characteristics for the selection of best models. By adopting this strategy, it is ensured that most accurate surrogate models are selected. There can be different kind of surrogate models for objective and constraint functions. Multi-objective optimization of machine tool spindle is studied as the test problem for this algorithm and it is observed that the proposed strategy is able to find the non- dominated solutions with minimum number of costly function evaluations. The developed method can be applied to other benchmark and engineering applications.

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