A Volatile RRAM Synapse for Neuromorphic Computing Neuromorphic computing has emerged as a promising approach for autonomous systems able to learn, adapt, and interact in real time with the environment. To build neuromorphic hardware, the recent development of novel materialbased devices such as resistive switching memory (RRAM) has shown to be crucial since this class of devices offers the unique advantage to implement neuron and synaptic functions in silico by device physics, thus avoiding bulky circuits and very complex algorithms. In this work, we first explore volatile switching behaviour of RRAM devices, investigating their ability to capture shortterm plasticity (STP) and shortterm memory (STM) functionalities. Then, we characterise a volatile RRAM synapse, discussing its potential use in a spiking neural network for speech recognition applications.

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.