Phase transition oxide neuron for spiking neural networks
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Spiking neural networks are expected to play a vital role in realizing ultra-low power hardware for computer vision applications [1]. While the algorithmic efficiency is promising, their solid-state implementation with traditional CMOS transistors lead to area expensive solutions. Transistors are typically designed and optimized to perform as switches and do not naturally exhibit the dynamical properties of neurons. In this work, we harness the abrupt insulator-to-metal transition (IMT) in a prototypical IMT material, vanadium dioxide (VO2) [2], to experimentally demonstrate a compact integrate and fire spiking neuron [3]. Further, we show multiple spiking dynamics of the neuron relevant to implementing `winner take all' max pooling layers employed in image processing pipelines.
[1] William Paul,et al. Optical and transport properties of high quality crystals of V2O4 near the metallic transition temperature , 1969 .
[2] Eugene M. Izhikevich,et al. Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.
[3] Andrew S. Cassidy,et al. A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.
[4] Vijaykrishnan Narayanan,et al. Synchronized charge oscillations in correlated electron systems , 2014, Scientific Reports.