An IP Core Mapping Algorithm Based on Neural Networks

Chen, Qingkun; Huang, Wenjin; Zhang, Yuanshan; Huang, Yapeng

doi:10.1109/tvlsi.2020.3033658

Cited by 14 publications

(8 citation statements)

References 27 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%

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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%

“…The authors in the paper [1], proposed pointer networks and RL based framework to solve the Traveling salesman Problem (TSP). Neural Mapping Algorithm (NMA) proposed in [5] inds the IP core mapping using pointer networks and RL. NMA requires large labeled data to train the neural networks.…”

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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“…In this work [9], the focus is on how to map the IP cores onto mesh NoC while minimizing the communication cost. A Neural Network IP core Mapping Model (NNMM) and a neural mapping algorithm (NMA) have been proposed.…”

Section: Related Workmentioning

confidence: 99%

“…For example, the two topology strings of MP3 Encoder application are considered. Difference {(2,0), (3,5), (4,7), (6,3), (8,11), (10,8), (16,9), (20,4)…”

Section: Irregular Topology Generationmentioning

confidence: 99%

“…In the list of random numbers generated the positions whose values are lower than β(r) are exchanged with the P best . Now the position after exchange is: {(3,5), (4,7), (6,3), (8,11), (16,9)}. So, after the exchange mechanism process, the topology is changed as {−1 7 5 7 −2 3 −1 11 −3 0 −2 -4 2 −4 1 9 10 6 −3 8}.…”

Section: Irregular Topology Generationmentioning

confidence: 99%

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Discrete Firefly Algorithm for Optimizing Topology Generation and Core Mapping of Network-on-Chip

Parvathi¹,

Umamaheswari²

2022

Intelligent Automation &Amp; Soft Computing

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Network-on-chip (NoC) proves to be the best alternative to replace the traditional bus-based interconnection in Multi-Processor System on a Chip (MPSoCs). Irregular NoC topologies are highly recommended and utilised in various applications as they are application specific. Optimized mapping of the cores in a NoC plays a major role in its performance. Firefly algorithm is a bio-inspired meta-heuristic approach. Discretized firefly algorithm is used in our proposed work. In this work, two optimization algorithms are proposed: Topology Generation using Discrete Firefly Algorithm (TGDFA) and Core Mapping using Discrete Firefly Algorithm (CMDFA) for multimedia benchmark applications, Video Object Plane Decoder (VOPD), Multimedia Window Display (MWD) and MP3 Encoder. The irregular topology generated using TGDFA is mapped using CMDFA onto a reconfigurable mesh with switches in between the routers to make it a fault tolerant one. The proposed TGDFA provides better optimization of communication cost. The speed-up on the average run time of the tasks and the time taken to attain the best solution by the proposed TGDFA is significant. The dynamic energy consumption of core mapping obtained by the proposed CMDFA is lesser when compared to the existing work. Finally, the optimized core mapping is implemented in a cycle accurate NoC simulator: Noxim. It is substantiated experimentally that the proposed CMDFA outperforms the previous work.

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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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An IP Core Mapping Algorithm Based on Neural Networks

Cited by 14 publications

References 27 publications

NoC Application Mapping Optimization Using Reinforcement Learning

NoC Application Mapping Optimization Using Reinforcement Learning

Discrete Firefly Algorithm for Optimizing Topology Generation and Core Mapping of Network-on-Chip

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

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