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Large-scale integration of emerging nanoscale non-volatile memory devices,e.g. resistive random-access memory (RRAM), can enable a new generation ofneuromorphic computers that can solve a wide range of machine learningproblems. Such hybrid CMOS-RRAM neuromorphic architectures will result inseveral orders of magnitude reduction in energy consumption at a very smallform factor, and herald autonomous learning machines capable of self-adaptingto their environment. However, the progress in this area has been impeded fromthe realization that the actual memory devices fall well short of theirexpected behavior. In this work, we discuss the challenges associated withthese memory devices and their use in neuromorphic computing circuits, andpropose pathways to overcome these limitations by introducing 'dendriticlearning'.

Enabling Bio-Plausible Multi-level STDP using CMOS Neurons with Dendrites and Bistable RRAMs