iHPSA: An improved bio-inspired hybrid optimization algorithm for task mapping in Network on Chip

Amin, Waqar; Hussain, Fawad; Anjum, Sheraz

doi:10.1016/j.micpro.2022.104493

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…Levy flying meta-heuristic cuckoo search was then used to optimize task placements for optimum mapping. In [27], the authors put forward the IHPSA optimization method, an enhanced hybrid PSO, and the simulated annealing technique. Enhanced Particle Swarm Optimization with SA was combined in the proposed IHPSA for application mapping.…”

Section: Related Workmentioning

confidence: 99%

IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip

Saleem,

Hussain,

Baloch

2024

Algorithms

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Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm’s efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm’s superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems.

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Section: Related Workmentioning

confidence: 99%

IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip

Saleem,

Hussain,

Baloch

2024

Algorithms

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“…This section presents the proposed hybrid application mapping and reconfiguration framework HyDra, which included dynamic mapping and reconfiguration DRA and design-time mapping approaches for the NoC-based multicore platform. The design-time mapping approach iHPSA [21] generates mappings with optimal communication costs having minimum latency and energy consumption. The design time stage produces different mappings that can have optimal communication costs.…”

Section: Hydramentioning

confidence: 99%

“…We used our earlier work iHPSA [21] an improved hybrid Particle Swarm and Simulated the Annealing based application mapping technique for 2D NoC as a design-time mapping framework. In the iHPSA, the PSO with strong global search capabilities and the SA algorithm with strong local search capabilities are combined.…”

Section: A Design-time Mapping Frameworkmentioning

confidence: 99%

HyDra: Hybrid Task Mapping Application Framework for NOC-Based MPSoCs

et al. 2023

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Multiprocessor System-On-Chip (MPSoCs) with Networks-on-Chip (NoCs) has been proposed to address the communication challenges in modern dynamic applications. One of the key aspects of design exploration in NoC-based MPSoC is application mapping, which is critical for the parallel execution of multiple applications. However, mapping for dynamic workloads becomes challenging due to the unpredictable arrival times of applications and the availability of resources. In this work, we propose a hybrid task mapping approach, HyDra, that combines design-time mapping and efficient runtime remapping to reduce communication and energy costs. The proposed approach generates multiple application mappings during the design phase by minimizing latency, energy, and communication costs. The diverse mapping possibilities produced at design time consider multiple performance metrics. However, we cannot predict the arrival time of applications and the availability of resources at design time. To further optimize the MPSoC performance, our dynamic mapping phase re-configures the design time mappings based on the runtime availability of resources and applications. The simulation results show that HyDra reduces communication costs by 14% while using 15% less energy for small and large NoCs compared to state-of-the-art task mapping techniques. Furthermore, our approach provides an average of 19% reduction in end-to-end latency for applications. Our hybrid task allocation and scheduling approach effectively addresses communication issues in NoC-based MPSoCs for dynamic workloads. HyDra achieves improved performance by combining design-time and runtime mapping, providing a promising solution for future MPSoC design.INDEX TERMS Hybrid application mapping, multiprocessors, network-on-chip, particle swarm optimization, simulated annealing, task graph for free, directed acyclic graph, dynamic task mapping, design-time mapping, K-means, elbow method.

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“…16 Further, they refined the mapping through a combination of genetic and simulated annealing algorithms. A few of the existing heuristic based mapping algorithms utilize evolutionary approaches like ant colony optimization (ACO), 17,18 particle swarm optimization (PSO), [19][20][21] and genetic algorithms. [22][23][24] Recently, application mapping also adopted many of the bio-inspired based search algorithms.…”

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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iHPSA: An improved bio-inspired hybrid optimization algorithm for task mapping in Network on Chip

Cited by 16 publications

References 23 publications

IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip

IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip

HyDra: Hybrid Task Mapping Application Framework for NOC-Based MPSoCs

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

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