Frequency behaviour of FEFET-based ultra-low-power coupled oscillator neurons

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Abstract

The inefficiencies of von-Neumann computing architectures in performing high-level machine learning and neuromorphic operations have led to the need for new computational systems such as oscillatory neural networks, that are designed using emerging technologies. The frequency dynamics of Ferroelectric field-effect transistor (FeFET)-based coupled oscillator neurons, as integral parts of these novel computational networks are investigated in this paper. The concept of time to frequency modulation is also introduced which can be used to distinguish dynamical changes in the incoming signals. Simulation results show a significant reduction in power consumption with approximately 9 μW for each oscillator neuron. This is significant improvement over results reported for spin-torque and metal-insulator-migration oxide-based coupled-oscillators. Integrability with CMOS technology in conjunction with lower power consumption and simpler topologies make FeFET devices promising candidates for the next generation of neuromorphic computing platforms, and particularly for image, video, and audio processing tasks.

Original languageEnglish
Title of host publication2020 IEEE International Symposium on Circuits and Systems (ISCAS)
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9781728133201
DOIs
Publication statusPublished - 2020
Event52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Virtual
Duration: 10 Oct 202021 Oct 2020

Publication series

Name
ISSN (Electronic)2158-1525

Conference

Conference52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
CityVirtual
Period10/10/2021/10/20

Keywords

  • FeFET Device
  • Coupled Oscillator
  • Neuromorphic Computing
  • CMOS Technology
  • Frequency Modulation

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