Application Mapping Using Cuckoo Search Optimization With Lévy Flight for NoC-Based System

Mohiz, Muhammad Junaid; Baloch, Naveed Khan; Hussain, Fawad; Saleem, Sharoon; Zikria, Yousaf Bin; Yu, Heejung

doi:10.1109/access.2021.3120079

Cited by 19 publications

(8 citation statements)

References 34 publications

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“…Fault-tolerant approaches such as [32][33][34][35][36][37][38][39][40] are trying to deal with various faults that may happen in the NoC environment and make unreliable NoCs safe environments that can execute the tasks successfully.…”

Section: Fault Tolerance-aware Schemesmentioning

confidence: 99%

“…This section studies task mapping schemes such as [37,[66][67][68][69][70], which attempt to consider communication overhead in the mapping process. For instance, the method for executing the mapping and scheduling the tasks which belong to an executable application for NoC-based MPSoC, beginning from its UML is presented in [71].…”

Section: Communication-aware Schemesmentioning

confidence: 99%

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Towards Task Mapping Approaches in Network on Chips: A Comprehensive Survey

Màsdàrí

2023

Preprint

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One of the main stages of Network on Chip (NoC) is task mapping where the application tasks are mapped to the processing resources. Task mapping decisions have a direct impact on the NoC performance and can be classified as static or dynamic mapping methods. In this paper, we carry out a comprehensive systematic study of task mapping schemes in various NoCs. For this purpose, firstly, an overview of the task mapping's main concepts is presented. Then, a taxonomy of the mapping schemes regarding their objectives and applied mapping factors is presented and the studied schemes are categorized into fault-tolerant schemes, congestion-aware schemes, communication-aware approaches, temperature-aware methods, and energy-aware schemes. Afterward, their main contributions, provided services, and drawbacks of the proposed studies in the literature are illustrated. Moreover, a comprehensive discussion of various features of the investigated mapping methods is presented and issues such as the applied algorithms, simulators, datasets, and evaluation metrics are discussed. Finally, the concluding issues and open research areas are highlighted.

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Section: Fault Tolerance-aware Schemesmentioning

confidence: 99%

Section: Communication-aware Schemesmentioning

confidence: 99%

Towards Task Mapping Approaches in Network on Chips: A Comprehensive Survey

Màsdàrí

2023

Preprint

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“…[22][23][24] Recently, application mapping also adopted many of the bio-inspired based search algorithms. [25][26][27][28] These evolutionary approaches face some difficulties such as slower convergence and higher CPU time.…”

Section: Related Workmentioning

confidence: 99%

“…A few of the existing heuristic based mapping algorithms utilize evolutionary approaches like ant colony optimization (ACO), 17,18 particle swarm optimization (PSO), 19‐21 and genetic algorithms 22‐24 . Recently, application mapping also adopted many of the bio‐inspired based search algorithms 25‐28 . These evolutionary approaches face some difficulties such as slower convergence and higher CPU time.…”

Section: Related Workmentioning

confidence: 99%

SpecMap: An efficient spectral partitioning based static application mapping algorithm for 2D mesh NoCs

R.S.

Jayaraj

2023

Concurrency and Computation

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SummaryNetwork‐on‐chip (NoC) is adopted as a flexible and effective communication backbone by multiprocessor systems with core counts ranging from a few to hundreds. The performance of NoC based systems greatly rely on the communication between cores when two dependent tasks of an application are mapped to these cores. Hence, application mapping becomes a critical issue in NoC‐based systems as it affects the overall performance of the system. Application mapping aims to reduce the communication among the cores by carefully mapping the highly communicating tasks. Most of the existing static mapping approaches consider only single‐task platforms. In this article, we propose an efficient multi‐task static mapping algorithm called , for regular 2D NoC systems. consists of three stages namely, (i) graph partitioning, (ii) topology clustering, and (iii) mapping. In the graph partitioning stage, accurately identifies and groups the highly communicating tasks based on spectral graph partitioning. Then, the clustering and mapping stages allocate the most communicating tasks in the same or adjacent multi‐tasking cores. It also eliminates the interference between multiple applications by mapping them into different clusters of the NoC network. Simulations of have been conducted on different synthetic and real‐life applications using Gem5 simulator. The simulation results show considerable improvements for SpecMap in terms of communication cost, average network latency, energy consumption, throughput, and execution time as compared to the state‐of‐the‐art algorithms.

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“…Rosli and Mohamed (2021) presented a developed CS approach with different spaces for optimizing the Bouc-Wen model in magnetorheological damper applications. Mohiz et al (2021) submitted an enhanced CS algorithm on the basis of a greedy strategy for optimizing task placement on a network-on-chip (NoC) core. Bibiks et al (2018) proposed an improved discrete CS (IDCS) to solve the resource-constrained project scheduling problems (RPs).…”

Section: Introductionmentioning

confidence: 99%

A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

Shou

et al. 2022

Front. Neuroinform.

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IntroductionPulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific.MethodsUsing the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient’s basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier.ResultsIn the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches.DiscussionThe experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE.

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Application Mapping Using Cuckoo Search Optimization With Lévy Flight for NoC-Based System

Cited by 19 publications

References 34 publications

Towards Task Mapping Approaches in Network on Chips: A Comprehensive Survey

Towards Task Mapping Approaches in Network on Chips: A Comprehensive Survey

SpecMap: An efficient spectral partitioning based static application mapping algorithm for 2D mesh NoCs

A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

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