Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM
Network-on-Chips (NoCs) serve as essential interconnection infrastructures in Multiprocessor System-on-Chip (MPSoC) designs, emphasizing flexibility, extensibility, and low power consumption. The effectiveness of communication within NoCs relies heavily on the routing algorithm employed. However,...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/119891/ http://psasir.upm.edu.my/id/eprint/119891/1/119891.pdf |
| _version_ | 1848868075371233280 |
|---|---|
| author | R Muhsin, Al-Molla Yousif |
| author_facet | R Muhsin, Al-Molla Yousif |
| author_sort | R Muhsin, Al-Molla Yousif |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Network-on-Chips (NoCs) serve as essential interconnection infrastructures in Multiprocessor
System-on-Chip (MPSoC) designs, emphasizing flexibility, extensibility,
and low power consumption. The effectiveness of communication within NoCs relies
heavily on the routing algorithm employed. However, the routing process faces
significant challenges, such as deadlock, livelock, congestion, and faults, which
impact the Design Space Exploration process. In addition, the selection of appropriate
and effective routing algorithms poses a challenge for designers due to multiple
criteria, data fluctuations, and the importance of varying criteria.
This study proposed a prediction model-based Artificial Neural Network (ANN) with
a Metaheuristic Optimization approach for predicting the utilized routing algorithm
by the NoC-based MPSoC platform in order to reduce the time required to specify the
NoC-based MPSoC platform configurations. Furthermore, the authors propose a
comprehensive assessment of various routing algorithms, aiming to identify the most
suitable and effective routing algorithm that satisfies designers’ system-level
requirements and assessment criteria.
The methodology includes two phases; phase 1 includes developing a prediction
model, specifically an ANN optimized using the Guaranteed Convergence Arithmetic
Optimization Algorithm (GCAOA-ANN). Whereas phase 2 integrates the fuzzyweighted
zero-inconsistency method and the fuzzy decision-by-opinion score method.
The utilisation of the Z-Cloud Rough Numbers environment addresses the challenge
of two types of uncertainty.
The study result shows that the phase 1 hybrid GCAOA-ANN model demonstrated
superior performance compared to other models. At the same time, a multi-criteria
decision-making (MCDM) approach (phase 2) analysis reveals that Adaptive
Dimensional Bubble Routing, Message-based Congestion-Aware Routing, and
Dynamic and Adaptive Routing Algorithms are ranked as the top three routing
algorithms, respectively. |
| first_indexed | 2025-11-15T14:46:37Z |
| format | Thesis |
| id | upm-119891 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:46:37Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1198912025-10-09T06:43:03Z http://psasir.upm.edu.my/id/eprint/119891/ Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM R Muhsin, Al-Molla Yousif Network-on-Chips (NoCs) serve as essential interconnection infrastructures in Multiprocessor System-on-Chip (MPSoC) designs, emphasizing flexibility, extensibility, and low power consumption. The effectiveness of communication within NoCs relies heavily on the routing algorithm employed. However, the routing process faces significant challenges, such as deadlock, livelock, congestion, and faults, which impact the Design Space Exploration process. In addition, the selection of appropriate and effective routing algorithms poses a challenge for designers due to multiple criteria, data fluctuations, and the importance of varying criteria. This study proposed a prediction model-based Artificial Neural Network (ANN) with a Metaheuristic Optimization approach for predicting the utilized routing algorithm by the NoC-based MPSoC platform in order to reduce the time required to specify the NoC-based MPSoC platform configurations. Furthermore, the authors propose a comprehensive assessment of various routing algorithms, aiming to identify the most suitable and effective routing algorithm that satisfies designers’ system-level requirements and assessment criteria. The methodology includes two phases; phase 1 includes developing a prediction model, specifically an ANN optimized using the Guaranteed Convergence Arithmetic Optimization Algorithm (GCAOA-ANN). Whereas phase 2 integrates the fuzzyweighted zero-inconsistency method and the fuzzy decision-by-opinion score method. The utilisation of the Z-Cloud Rough Numbers environment addresses the challenge of two types of uncertainty. The study result shows that the phase 1 hybrid GCAOA-ANN model demonstrated superior performance compared to other models. At the same time, a multi-criteria decision-making (MCDM) approach (phase 2) analysis reveals that Adaptive Dimensional Bubble Routing, Message-based Congestion-Aware Routing, and Dynamic and Adaptive Routing Algorithms are ranked as the top three routing algorithms, respectively. 2024-06 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/119891/1/119891.pdf R Muhsin, Al-Molla Yousif (2024) Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM. Doctoral thesis, Universiti Putra Malaysia. http://ethesis.upm.edu.my/id/eprint/18485 Multiprocessors Computer networks--Routing Neural networks (Computer science) |
| spellingShingle | Multiprocessors Computer networks--Routing Neural networks (Computer science) R Muhsin, Al-Molla Yousif Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM |
| title | Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM |
| title_full | Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM |
| title_fullStr | Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM |
| title_full_unstemmed | Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM |
| title_short | Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM |
| title_sort | benchmarking routing algorithms in noc-based mpsocs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy mcdm |
| topic | Multiprocessors Computer networks--Routing Neural networks (Computer science) |
| url | http://psasir.upm.edu.my/id/eprint/119891/ http://psasir.upm.edu.my/id/eprint/119891/ http://psasir.upm.edu.my/id/eprint/119891/1/119891.pdf |