A Reinforcement Learning-Based Framework for Solving the IP Mapping Problem

Chen, Qingkun; Huang, Wenjin; Peng, Yuze; Huang, Yapeng

doi:10.1109/tvlsi.2021.3097712

Cited by 17 publications

(9 citation statements)

References 29 publications

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“…From Table 5, it is observed that the average network latency for the mappings obtained using RL-MAP is less than that of heuristic algorithms. This is because the mappings obtained from RL-MAP have less overall communication cost compared to the heuristic algorithms, which resulted in less Comparison With ML algorithms: In this section the RL-MAP is compared with the ML based NMA [5] and MPN-GA [4]. Table 6 presents the overall communication cost and run time of the RL-MAP, NMA and MPN-GA.…”

Section: Resultsmentioning

confidence: 99%

“…NMA requires large labeled data to train the neural networks. In [4], the authors proposed message-passing neural network-pointer network-based genetic algorithm (MPN-GA) and message passing neural network-pointer network-based PSMAP (MPN-PSMAP). These RL-based frameworks are built by combining the message passing neural networks, pointer networks and a heuristic algorithm.…”

Section: Related Workmentioning

confidence: 99%

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NoC Application Mapping Optimization Using Reinforcement Learning

Samala

Bhanu

Soumya

2022

ACM Trans. Des. Autom. Electron. Syst.

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Application mapping is one of the early stage design processes aimed to improve the performance of Network-on-Chip (NoC). Mapping is an NP-hard problem. A massive amount of high-quality supervised data is required to solve the application mapping problem using traditional neural networks. In this article, a Reinforce Learning based neural framework is proposed to learn the heuristics of the application mapping problem. The proposed reinforcement learning based mapping algorithm (RL-MAP) has actor and critic networks. The Actor is a policy network, which provides mapping sequences. The critic network estimates the communication cost of these mapping sequences. The actor network updates the policy distribution in the direction suggested by the critic. The proposed RL-MAP is trained with unsupervised data to predict the permutations of the cores to minimize the overall communication cost. Further, the solutions are improved using the 2-opt local search algorithm. The performance of RL-MAP is compared with a few well-known heuristic algorithms, Neural Mapping Algorithm (NMA) and message-passing neural network-pointer network-based genetic algorithm (MPN-GA). Results show that the communication cost and run-time of the RL-MAP improved considerably in comparison with the heuristic algorithms. The communication cost of the solutions generated by RL-MAP is nearly equal to MPN-GA and improved by 4.2% over NMA, while consuming less run time.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

NoC Application Mapping Optimization Using Reinforcement Learning

Samala

Bhanu

Soumya

2022

ACM Trans. Des. Autom. Electron. Syst.

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“…If the initial population generation is not effective, incremental improvement towards finding an optimal solution will be limited. In swarmbased intelligence optimization techniques, the initial population quality has a significant impact on the algorithm's speed and accuracy [39,40]. In the proposed work, the WOA algorithm is modified in terms of initial mapping generation, and is then employed to enhance overall application mapping to NoC Cores.…”

Section: Inspiration For Noc Application Mappingmentioning

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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“…Chen et al proposed neural mapping algorithm (NMA), 37 and a message-passing neural network-pointer network-based genetic algorithm (MPN-GA). 38 Both these mapping algorithms are built by combining message-passing neural networks and pointer networks to generate the initial population, and a heuristic algorithm to find the refined solution. The RL framework based on the Actor-Critic method to optimize the application mapping problem is also presented.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, researchers have also used machine learning techniques and neural networks to solve mapping problems. Chen et al proposed neural mapping algorithm (NMA), 37 and a message‐passing neural network‐pointer network‐based genetic algorithm (MPN‐GA) 38 . Both these mapping algorithms are built by combining message‐passing neural networks and pointer networks to generate the initial population, and a heuristic algorithm to find the refined solution.…”

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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A Reinforcement Learning-Based Framework for Solving the IP Mapping Problem

Cited by 17 publications

References 29 publications

NoC Application Mapping Optimization Using Reinforcement Learning

NoC Application Mapping Optimization Using Reinforcement Learning

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

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

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