“…Recently, several types of emerging electronic synapse devices such as phase change memory (PCRAM) (Suri et al, 2011;Wright et al, 2011;Kuzum et al, 2012), resistive change memory (RRAM) (Jo et al, 2010;Ohno et al, 2011;Wu et al, 2012;Yu et al, 2012), ferroelectric devices (Chanthbouala et al, 2012), and FET-based devices (Diorio et al, 1996;Ziegler et al, 2012;Kim et al, 2016) have been proposed to mimic biological synapses. Although most of these works have focused on spike-timingdependent-plasticity (STDP, (Bi and Poo, 1998)) learning algorithm (Kuzum et al, 2013), the learning performance using STDP is still in its early stage (Burr et al, 2014;Nair and Dudek, 2015). Unlike the approach in which STDP is used, electronic synapse devices (Burr et al, 2014;Prezioso et al, 2015;Merrikh-Bayat et al, 2015) can also be applied to deep neural networks (DNNs) with well-studied back-propagation (BP) algorithms (Rumelhart et al, 1986).…”