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.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was partially funded by ONE Lab at Zewail City of Science and Technology and at Cairo University, NTRA, ITIDA, ASRT, and NSERC.