ONE Lab would like to congratulate Eng. Salma Hassan (ONE Lab current Master student) and Eng. Sameh Attia (ONE Lab former RA and currently PhD student at the University of Toronto, Canada) on their accepted journal paper at MDPI Electronics Journal titled “ EANN: Energy Adaptive Neural Networks”. The paper Abstract is below:
This paper proposes an Energy Adaptive Feedforward Neural Network (EANN). It utilizes multiple approximation techniques in the hardware implementation of the neuron unit . The utilized 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 use 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 can adapt the hardware units to the degraded energy the expense of degraded accuracy.
