We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by providing faster convergence, higher accuracy and a flexible long short-term memory. We present a hardware efficient methodology to realize the DEXAT neurons using tightly coupled circuit-device interactions and experimentally demonstrate the DEXAT neuron block using oxide based non-filamentary resistive switching devices. Using experimentally extracted parameters we simulate a full RSNN that achieves a classification accuracy of 96.1% on SMNIST dataset and 91% on Google Speech Commands (GSC) dataset. We also demonstrate full end-to-end real-time inference for speech recognition using real fabricated resistive memory circuit based DEXAT neurons. Finally, we investigate the impact of nanodevice variability and endurance illustrating the robustness of DEXAT based RSNNs.

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The authors present a unique application of analogue oxide-based resistive memory (OxRAM) device for sensor-level information storage and computation. They show that quality of low-contrast images in low-light can be improved by carefully exploiting OxRAM conductance modulation from specific bi-layer OxRAM material stacks. The proposed methodology involves conversion of light intensity to pulse frequency followed by resistance encoding as different non-volatile OxRAM resistance states.

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