Nonfilamentary nonvolatile memory elements as synapses in neuromorphic systems Crossbar arrays of nonvolatile memory (NVM) devices represent one possible path for implementing highly energyefficient neuromorphic computing systems. For Deep Neural Networks (DNN), where information can be encoded as analog voltage and current levels, such arrays can represent matrices of synaptic weights, implementing the matrixvector multiplication needed for algorithms such as backpropagation in a massivelyparallel fashion. Previous research demonstrated a largescale hardwaresoftware implementation based on phasechange memories and analyzed the potential speed and power advantages over GPUbased training. In this proceeding we will discuss extensions of this work leveraging a different class of memory elements. Using the concept of jumptables we simulate the impact of real conductance response of nonfilamentary resistive devices based on P r_0._3Ca_0.7 Mn O_3 (PCMO). With the same approach as of 1, we simulate a threelayer neural network with training accuracy u003e90% on the MNIST dataset. The higher ONOFF conductance ratio of improved AlMoPCMO devices together with new programming strategies can lead to further accuracy improvement. Finally, we show that the bidirectional programming of AlMoPCMO can be used to implement highdensity neuromorphic systems with a single conductance per synapse, at only a slight degradation to accuracy.

PhaseChange Memory Models for Deep Learning Training and Inference Nonvolatile analog memory devices such as phasechange memory (PCM) enable designing dedicated connectivity matrices for the hardware implementation of deep neural networks (DNN). In this inmemory computing approach, the analog conductance states of the memory device can be gradually updated to train DNNs onchip or software trained connection strengths may be programmed onetime to the devices to create efficient inference engines. Reliable and computationally simple models that capture the nonideal programming and temporal evolution of the devices are needed for evaluating the training and inference performance of the deep learning hardware based on inmemory computing. In this paper, we present statistically accurate models for PCM, based on the characterization of more than 10,000 devices, that capture the statedependent nature and variability of the conductance update, conductance drift, and read noise. Integrating the computationally simple device models with deep learning frameworks such as TensorFlow enables us to realistically evaluate training and inference performance of the PCM array based hardware implementations of DNNs.