A New Discrete-Time Multi-Constrained K -Winner-Take-All Recurrent Network and Its Application to Prioritized Scheduling

In this paper, we propose a novel discrete-time recurrent neural network aiming to resolve a new class of multi-constrained K-winner-take-all (K-WTA) problems. By facilitating specially designed asymmetric neuron weights, the proposed model is capable of operating in a fully parallel manner, thereby...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 28; H. 11; S. 2674 - 2685
1. Verfasser: Tien, Po-Lung
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.11.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">K -winner take all
Biological neural networks
Computational modeling
Computer simulation
Convergence
Data processing
Energy consumption
Energy saving
Latency
Neural networks
Neurons
parallel computation
prioritized scheduling
Quality of service
quality of service (QoS)
Radio frequency
recurrent neural network
Recurrent neural networks
Routing
Scheduling
Take-all disease
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:In this paper, we propose a novel discrete-time recurrent neural network aiming to resolve a new class of multi-constrained K-winner-take-all (K-WTA) problems. By facilitating specially designed asymmetric neuron weights, the proposed model is capable of operating in a fully parallel manner, thereby allowing true digital implementation. This paper also provides theorems that delineate the theoretical upper bound of the convergence latency, which is merely O(K). Importantly, via simulations, the average convergence time is close to O(1) in most general cases. Moreover, as the multi-constrained K-WTA problem degenerates to a traditional single-constrained problem, the upper bound becomes exactly two parallel iterations, which significantly outperforms the existing K-WTA models. By associating the neurons and neuron weights with routing paths and path priorities, respectively, we then apply the model to a prioritized flow scheduler for the data center networks. Through extensive simulations, we demonstrate that the proposed scheduler converges to the equilibrium state within near-constant time for different scales of networks while achieving maximal throughput, quality-of-service priority differentiation, and minimum energy consumption, subject to the flow contention-free constraints.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2016.2600410