scholarly journals Intelligent Mapping Method for Power Consumption and Delay Optimization Based on Heterogeneous NoC Platform

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 912 ◽  
Author(s):  
Juan Fang ◽  
Huan Zong ◽  
Haoyan Zhao ◽  
Huayi Cai
Keyword(s):  
Genetic Algorithm ◽  
Power Consumption ◽  
Hot Spot ◽  
Mapping Method ◽  
Research Field ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mapping Problem ◽  
On Chip

As integrated circuit processes become more advanced, feature sizes become smaller and smaller, and more and more processing cores and memory components are integrated on a single chip. However, the traditional bus-based System-on-Chip (SoC) communication is inefficient, has poor scalability, and cannot handle the communication tasks between the processing cores well. Network-on-chip (NoC) has become an important development direction in this field by virtue of its efficient transmission and scalability of data between multiple cores. The mapping problem is a hot spot in NoC's research field, and its mapping results will directly affect the power consumption, latency, and other properties of the chip. The mapping problem is a NP-hard problem, so how to effectively obtain low-power and low-latency mapping schemes becomes a research difficulty. Aiming at this problem, this paper proposes a two-populations-with-enhanced-initial-population based on genetic algorithm (TI_GA) task mapping algorithm based on an improved genetic algorithm from the two indexes of power consumption and delay. The quality of the initial individual is optimized in the process of constructing the population, so the quality of initial population is improved. In addition, a two-population genetic mechanism is added during the iterative process of the algorithm. The experimental results show that TI_GA is very effective for optimizing network power consumption and delay of heterogeneous multi-core.

scholarly journals 3D NoC Low-Power Mapping Optimization Based on Improved Genetic Algorithm

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1217
Author(s):  
Yu Gan ◽  
Hong Guo ◽  
Ziheng Zhou
Keyword(s):  
Genetic Algorithm ◽  
Power Consumption ◽  
Low Power ◽  
Simulated Annealing Algorithm ◽  
Average Power ◽  
Global Optimum ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Average Power Consumption ◽  
Ip Cores

Power optimization is an important part of network-on-chip(NoC) design. This paper proposes an improved algorithm based on genetic algorithm on how to properly map IP (Intellectual Property) cores to 3D NoC. First, in view of the randomness of the traditional genetic algorithm in individual selection, an improved greedy algorithm is used in the initial population generation stage to make the generated individuals reach the optimal. Secondly, in view of the weak local optimization ability of the traditional genetic algorithm and prone to premature problems, the simulated annealing algorithm is added in the crossover operation stage to make the offspring reach the global optimum. The experimental results show that compared with the traditional genetic algorithm, the algorithm has better convergence and low power consumption performance, which can quickly search for a better solution, in the case of a large number of cores (124 IP cores), the average power consumption can be reduced by 42.2%.

scholarly journals Applying Genetic Algorithm to Solve Partitioning and Mapping Problem for Mesh Network-on-Chip Systems

2021 ◽  
Vol 13 (1) ◽  
pp. 33-47
Author(s):  
Walid Mokthar Salh ◽  
Azeddien M. Sllame
Keyword(s):  
Genetic Algorithm ◽  
Power Consumption ◽  
Design Process ◽  
Network On Chip ◽  
Experimental Results ◽  
Mesh Network ◽  
Data Dependencies ◽  
Mapping Problem ◽  
Mesh Topology ◽  
On Chip

This paper presents a genetic based approach to the partitioning and mapping of multicore SoC cores over a NoC system that uses mesh topology. The proposed algorithm performs the partitioning and mapping by reducing communication cost and minimizing power consumption by placing those intercommunicated cores as close as possible together. A program developed in C++ in which the provided specification of the multicore MPSoC system captures all data dependencies before any start of the design process. Experimental results of several multimedia benchmarks demonstrates that the genetic-based approach able to find different satisfied implementations to the problem of partitioning and mapping of MPSoC cores over mesh-based NoC system that satisfies design goals.

scholarly journals Improved Simulated Annealing Genetic Algorithm based Low power mapping for 3D NoC

2018 ◽  
Vol 232 ◽  
pp. 02022 ◽  
Author(s):  
Hanna He ◽  
Fang Fang ◽  
Wei Wang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Power Consumption ◽  
Average Power ◽  
Optimal Solution ◽  
Initial Population ◽  
Good Convergence ◽  
Average Power Consumption ◽  
Ip Cores ◽  
On Chip

Mapping of IP(Intellectual Property) cores onto NoC(Network-on-Chip) architectures is a key step in NoCbased designs. Energy is the key parameter to measure the designs. Therefore, we propose an Improved Simulated Annealing Genetic Alogrithm, abbreviated as ISAGA. The algorithm combines the parallelism of Genetic Algorithm(GA) and the local search ability of Simulated Annealing(SA). We improve the initial population selection of GA to get the lower power consumption mapping scheme. The experimental results show that compared with the GA, ISAGA has good convergence and can search the optimal solution quickly, which can effectively reduce the power consumption of the system. In the case of 124 IP cores, the average power consumption of the ISAGA is reduced by 32.0% compared with the GA.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Reconfigurable on-chip communication link for efficient communication

2018 ◽  
Vol 7 (2-1) ◽  
pp. 417
Author(s):  
Beulah Hemalatha S ◽  
Vigneswaran T
Keyword(s):  
Genetic Algorithm ◽  
Power Consumption ◽  
Low Power ◽  
Optimization Problems ◽  
Research Area ◽  
Low Power Consumption ◽  
Communication Link ◽  
Simulated System ◽  
Efficient Communication ◽  
On Chip

Application specific reconfiguration of On-chip communication link is a fast growing research area in system on chip (SoC) based system design. Optimization of the communication link is important to achieve a trade-off between efficient communication and low power consumption. So achieving both efficient communication and low power consumption requires a special optimization mechanism. Such Optimization problems can be solved using a genetic algorithm. Here, in this paper genetic algorithm based On-chip communication link reconfiguration is presented. The algorithm will optimize efficiency of communication link with constrain of low power consumption. The parameters involved in power consumption and efficient communication link are coded in the chromosomes. By evolutionary iteration the optimal parameters of the communication link are derived that is used for the communication link successfully in the simulated system. The performance of the simulated system is analyzed which shows the out performance of the proposed system.

Robot path planning based on genetic algorithm with hybrid initialization method

2021 ◽  
pp. 1-16
Author(s):  
Zhaojun Zhang ◽  
Rui Lu ◽  
Minglong Zhao ◽  
Shengyang Luan ◽  
Ming Bu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Iteration Process ◽  
Initial Population ◽  
Local Optimum ◽  
Complex Environments ◽  
Random Initialization ◽  
Planning Problems ◽  
Complete Path

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.

Topological Design Using Genetic Algorithms

2013 ◽  
pp. 153-173
Author(s):  
Rui Manuel Morais ◽  
Armando Nolasco Pinto
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Capital Expenditure ◽  
Initial Population ◽  
Minimum Capital ◽  
Topological Design ◽  
Node Location ◽  
Telecommunications Services ◽  
Resilient Networks

The proliferation of Internet access and the appearance of new telecommunications services are originating a demand for resilient networks with extremely high capacity. Thus, topologies able to recover connections in case of failure are essential. Given the node location and the traffic matrix, the survivable topological design is the problem of determining the network topology at minimum capital expenditure such that survivability is ensured. This problem is strongly NP-hard and heuristics are traditionally used to search near-optimal solutions. The authors present a genetic algorithm for this problem. As the convergence of the genetic algorithm depends on the used operators, an analysis of their impact on the quality of the obtained solutions is presented as well. Two initial population generators, two selection methods, two crossover operators, and two population sizes are compared, and the quality of the obtained solutions is assessed using an integer linear programming model.

Dynamically Reconfigurable Networks-on-Chip Using Runtime Adaptive Routers

2010 ◽  
pp. 28-66 ◽  
Author(s):  
Mário P. Véstias ◽  
Horácio C. Neto
Keyword(s):  
Quality Of Service ◽  
Power Consumption ◽  
First Generation ◽  
Networks On Chip ◽  
Dynamically Reconfigurable ◽  
Systems On Chip ◽  
High Bandwidth ◽  
Area Occupation ◽  
On Chip

The recent advances in IC technology have made it possible to implement systems with dozens or even hundreds of cores in a single chip. With such a large number of cores communicating with each other there is a strong pressure over the communication infrastructure to deliver high bandwidth, low latency, low power consumption and quality of service to guarantee real-time functionality. Networks-on-Chip are definitely becoming the only acceptable interconnection structure for today’s multiprocessor systems-on-chip (MPSoC). The first generation of NoC solutions considers a regular topology, typically a 2D mesh. Routers and network interfaces are mainly homogeneous so that they can be easily scaled up and modular design is facilitated. All advantages of a NoC infrastructure were proved with this first generation of NoC solutions. However, NoCs have a relative area and speed overhead. Application specific systems can benefit from heterogeneous communication infrastructures providing high bandwidth in a localized fashion where it is needed with improved area. The efficiency of both homogeneous and heterogeneous solutions can be improved if runtime changes are considered. Dynamically or runtime reconfigurable NoCs are the second generation of NoCs since they represent a new set of benefits in terms of area overhead, performance, power consumption, fault tolerance and quality of service compared to the previous generation where the architecture is decided at design time. This chapter discusses the static and runtime customization of routers and presents results with networks-on-chip with static and adaptive routers. Runtime adaptive techniques are analyzed and compared to each other in terms of area occupation and performance. The results and the discussion presented in this chapter show that dynamically adaptive routers are fundamental in the design of NoCs to satisfy the requirements of today’s systems-on-chip.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

2013 ◽  
Vol 365-366 ◽  
pp. 194-198 ◽  
Author(s):  
Mei Ni Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Simulation Software ◽  
Initial Population ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Search Speed ◽  
Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

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