LAUSR

A diffusive memristor is a promising building block for brain-inspired computing hardware. However, the randomness in the device dynamics limits the wide-range adoption of diffusive memristors in large arrays. In this work, we engineered the device stack to achieve a much-improved uniformity in the relaxation time (standard deviation σ reduced from ∼12 to ∼0.32 ms). We further connected the memristor with a resistor or a capacitor and tuned the relaxation time between 1.13 μs to 1.25 ms, ranging from three orders of magnitude. We implemented the hierarchy of time surfaces (HOTS) algorithm to utilize the tunable and uniform relaxation behavior for spike generation. We achieved 77.3% accuracy in recognizing moving objects in the neuromorphic MNIST (N-MNIST) dataset. Our work paves the way for building emerging neuromorphic computing hardware systems with ultralow power consumption. This article is protected by copyright. All rights reserved.