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Article

EANN: Energy Adaptive Neural Networks

1
Department of Electronics and Communications Engineering, Cairo University, Giza 12613, Egypt
2
Electrical Engineering Program, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
3
Nanotechnology and Nanoelectronics Program, University of Science and Technology, Zewail City of Science and Technology, October Gardens, 6th of October, Giza 12578, Egypt
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 746; https://doi.org/10.3390/electronics9050746
Received: 12 March 2020 / Revised: 25 April 2020 / Accepted: 29 April 2020 / Published: 1 May 2020
(This article belongs to the Section Artificial Intelligence)
This paper proposes an Energy Adaptive Feedforward Neural Network (EANN). It uses multiple approximation techniques in the hardware implementation of the neuron unit. The used techniques are precision scaling, approximate multiplier, computation skipping, neuron skipping, activation function approximation and truncated accumulation. The proposed EANN system applies the partial dynamic reconfiguration (PDR) feature supported by the FPGA platform to reconfigure the hardware elements of the neural network based on the energy budget. The PDR technique enables the EANN system to remain functioning when the available energy budget is reduced by factors of 46.2% to 79.8% of the total energy of the unapproximated neural network. Unlike the conventional operation that only uses certain amount of energy and cannot function properly if the energy budget falls below that energy level, the EANN system remains functioning for longer time after energy drop at the expense of less accuracy. The proposed EANN system is highly recommended in limited-energy applications as it adapts the hardware units to the degraded energy at the expense of some accuracy loss. View Full-Text
Keywords: ANN; approximate computing; partial dynamic reconfiguration; energy adaptive ANN; approximate computing; partial dynamic reconfiguration; energy adaptive
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MDPI and ACS Style

Hassan, S.; Attia, S.; Salama, K.N.; Mostafa, H. EANN: Energy Adaptive Neural Networks. Electronics 2020, 9, 746. https://doi.org/10.3390/electronics9050746

AMA Style

Hassan S, Attia S, Salama KN, Mostafa H. EANN: Energy Adaptive Neural Networks. Electronics. 2020; 9(5):746. https://doi.org/10.3390/electronics9050746

Chicago/Turabian Style

Hassan, Salma; Attia, Sameh; Salama, Khaled N.; Mostafa, Hassan. 2020. "EANN: Energy Adaptive Neural Networks" Electronics 9, no. 5: 746. https://doi.org/10.3390/electronics9050746

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