An Analog and Time-Discrete Neuron with Charge-Injection for Use in Ultra-Low Power Spiking Neural Networks

Matthias Ochs, Markus Dietl, Ralf Brederlow

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper shows a new concept for a neuron circuit for use in artificial spiking neural networks. The circuit performs the simplified analog calculations (LIF-neuron) of a biological neuron but uses advanced analog circuit design techniques to achieve functionality in a very small area and with large-scale integration in CMOS technology in mind. This neuron consumes only a tiny amount of power and is well suited for use within a spiking neural network in smart, ultra-low energy sensing applications. Due to the mapping of a spike event directly to a central clocking signal, large-scale integration and the mapping of weights using standard artificial intelligence algorithms have been drastically simplified. Simulations show that the analog computation concept can save a lot of energy.

Original languageEnglish
Title of host publication2024 19th Conference on Ph.D Research in Microelectronics and Electronics, PRIME 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350386301
DOIs
StatePublished - 2024
Event19th Conference on Ph.D Research in Microelectronics and Electronics, PRIME 2024 - Larnaca, Cyprus
Duration: 9 Jun 202412 Jun 2024

Publication series

Name2024 19th Conference on Ph.D Research in Microelectronics and Electronics, PRIME 2024

Conference

Conference19th Conference on Ph.D Research in Microelectronics and Electronics, PRIME 2024
Country/TerritoryCyprus
CityLarnaca
Period9/06/2412/06/24

Keywords

  • Neuromorphic HW
  • SNN
  • analog

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