Energy and thermal aware mapping for mesh-based NoC architectures using multi-objective ant colony algorithm

Liu, Yanhua; Ying, Rendong; Lai, Zongsheng; Jing, Weiping

doi:10.1109/iccrd.2011.5764225

Cited by 11 publications

(3 citation statements)

References 17 publications

(17 reference statements)

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“…Therefore, eliminating hotspot to achieve thermal balance is as important as minimizing the energy consumption when mapping the IP cores [13]. Because the spatial thermal decays exponentially with the Euclidean distance [14], the temperature model is proposed as follows:…”

Section: Temperature Modelmentioning

confidence: 99%

An efficient energy and thermal-aware mapping for regular network-on-chip

Liu

Zhu

et al. 2017

IEICE Electron. Express

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Chip temperature and energy consumption become one of the most critical design issues with technology scaling to nanometre-scale, especially for NoC systems with large number of cores and shrunken core size. To balance the temperature and energy consumption on NoC-based multi-cores system, this paper proposes an efficient NoC mapping approach in which the hyper-heuristic algorithm based on genetic operators (HAGO) is the core of this approach. Compared to simulated annealing algorithm and genetic algorithm, HAGO demonstrates a faster convergence speed and excellent stability. Experimental results show that our proposed mapping approach can make a better balance between energy consumption and temperature, which reduces the peak temperature from 365.2 K to 352 K and only increases the energy consumption by 1.12%.

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Section: Temperature Modelmentioning

confidence: 99%

An efficient energy and thermal-aware mapping for regular network-on-chip

Liu

Zhu

et al. 2017

IEICE Electron. Express

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“…Wang et al proposed a new effective optimization method based on the discrete particle swarm optimization framework, including the novel principles for representation, velocity computing and position-updating of the particles [14]. Liu et al proposed a multi-objective ant colony algorithm (MOACA) that mapped IP cores onto mesh-based NoC architectures, which showed to be an efficient way of finding the pareto-optimal front-optimizing energy consumption and hotspot temperature of an NoC [15].…”

Section: Introductionmentioning

confidence: 99%

New Heuristic Algorithm for Low Energy Mapping for 2.5-D Integration

et al. 2022

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A chiplet placement algorithm for 2.5-D IC integration on an interposer is discussed in this paper. Inspired by the NoC (network-on-chip) mapping problem, we propose a novel chiplet placement algorithm called the CCEOA (chiplet communication energy optimization algorithm), which takes into account the actual size of the chiplet. The CCEOA can map chiplets to mesh topology, resulting in a layout with a low CEC (communication energy consumption). The algorithm considers the spacing of the chiplets while selecting the initial nodes and the nodes to map the next chiplet. Furthermore, because there exist nodes resulting in the same CEC increment during the mapping process, the algorithm adopts a secondary local exploration strategy to further select nodes. Meanwhile, the lateral and vertical placements of chiplets are also considered. The algorithm is implemented and evaluated with a 2.5-D IC integration with 22 chiplets to demonstrate its efficiency and the accuracy.

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“…Authors in [8] introduce a heuristic core mapping algorithm called Spiral to improve energy consumption. A multi-objective Ant Colony Optimization algorithm (ACO) is presented for optimizing energy consumption and hotspot temperature of NoC in [9].…”

Section: Introductionmentioning

confidence: 99%

WOAGA: A new metaheuristic mapping algorithm for large-scale mesh-based NoC

Wang

Sun

et al. 2018

IEICE Electron. Express

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The mapping of IP cores to the topology is one of the most important steps for NoC (Network-on-Chip) design. Metaheuristic algorithms (MAs) are widely employed since the mapping is an NP-hard problem. Most mapping algorithms only consider small-scale NoC and ignore stability. In this letter, a stable metaheuristic algorithm called WOAGA, based on Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA), is proposed for large-scale NoC mapping to achieve the low-energy consumption and stability. In the proposed algorithm, irregular crossover and mutation operations are integrated into the modified WOA. A perturbation is utilized to jump out of local optima effectively. Simulation results show that the proposed algorithm is more stable and achieve better solution with energy consumption reduced significantly.

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Energy and thermal aware mapping for mesh-based NoC architectures using multi-objective ant colony algorithm

Cited by 11 publications

References 17 publications

An efficient energy and thermal-aware mapping for regular network-on-chip

An efficient energy and thermal-aware mapping for regular network-on-chip

New Heuristic Algorithm for Low Energy Mapping for 2.5-D Integration

WOAGA: A new metaheuristic mapping algorithm for large-scale mesh-based NoC

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