Compact oscillation neuron exploiting metal-insulator-transition for neuromorphic computing

Abstract

The phenomenon of metal-insulator-transition (MIT) in strongly correlated oxides, such as NbO<inf>2</inf>, have shown the oscillation behavior in recent experiments. In this work, the MIT based two-terminal device is proposed as a compact oscillation neuron for the parallel read operation from the resistive synaptic array. The weighted sum is represented by the frequency of the oscillation neuron. Compared to the complex CMOS integrate-and-fire neuron with tens of transistors, the oscillation neuron achieves significant area reduction, thereby alleviating the column pitch matching problem of the peripheral circuitry in resistive memories. Firstly, the impact of MIT device characteristics on the weighted sum accuracy is investigated when the oscillation neuron is connected to a single resistive synaptic device. Secondly, the array-level performance is explored when the oscillation neurons are connected to the resistive synaptic array. To address the interference of oscillation between columns in simple cross-point arrays, a 2-transistor-1-resistor (2T1R) array architecture is proposed at negligible increase in array area. Finally, the circuit-level benchmark of the proposed oscillation neuron with the CMOS neuron is performed. At single neuron node level, oscillation neuron shows &#x003E;12.5&#x00D7; reduction of area. At 128&#x00D7;128 array level, oscillation neuron shows a reduction of &#x223C;4% total area, &#x003E;30% latency, &#x223C;5&#x00D7; energy and &#x223C;40&#x00D7; leakage power, demonstrating its advantage of being integrated into the resistive synaptic array for neuro-inspired computing.