Phase Coupling and Control of Oxide-Based Oscillators for Neuromorphic Computing

Abstract

Neuromorphic computing using neural network hardware has attracted significant interest as it promises improved performance at low power for data-intensive error-resilient graphical signal processing. Oscillatory neural networks (ONNs) use either frequency or phase as state variables to implement frequency-shift keying (FSK)- and phase-shift keying (PSK)-based neural networks, respectively. To make these ONNs power and area efficient, back-end-of-the-line compatible, and capable of processing multilevel information, we explore an emerging class of oscillators that show fine-grain frequency-tuning and phase-coupling. We examine TaOx- and TiOx-based oscillators (resistive random access memory-type) as elements of a neuromorphic compute block and experimentally demonstrate: 1) frequency control over four decades using a ballast MOSFET; 2) variable phase coupling between oscillators; and 3) variable phase programming between oscillators coupled with a MOSFET. Such fine-grain control over both frequency and relative phase serve as the desirable characteristics of oscillators required for multilevel information processing in star-type directly coupled FSK- and PSK-based neuromorphic systems that find applications in gray-scale image processing and other graphical compute paradigms. These attributes combined with the small size (<;1 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and simplicity, make these devices attractive candidates for realizing large-scale neuromorphic systems at reasonable size and power.