IEEE Journal on Exploratory Solid-State Computational Devices and Circuits (Jan 2020)

Convolution Inference via Synchronization of a Coupled CMOS Oscillator Array

  • Dmitri E. Nikonov,
  • Peter Kurahashi,
  • James S. Ayers,
  • Hai Li,
  • Telesphor Kamgaing,
  • Georgios C. Dogiamis,
  • Hyung-Jin Lee,
  • Yongping Fan,
  • I. A. Young

DOI
https://doi.org/10.1109/JXCDC.2020.3046143
Journal volume & issue
Vol. 6, no. 2
pp. 170 – 176

Abstract

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Oscillator neural networks (ONNs) are a promising hardware option for artificial intelligence. With an abundance of theoretical treatments of ONNs, few experimental implementations exist to date. In contrast to prior publications of only building block functionality, we report a practical experimental demonstration of neural computing using an ONN. The arrays contain 26 CMOS ring oscillators in the GHz range of frequencies are tuned by image data and convolution kernels. Synchronization of oscillators results in an analog output voltage approximating convolution neural network operation.

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