A reconfigurable mixed-signal implementation of a neuromorphic ADC

Ying Xu, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, Runchun Wang, André van Schaik

    Research output: Chapter in Book / Conference PaperConference Paperpeer-review

    3 Citations (Scopus)

    Abstract

    ![CDATA[We present a neuromorphic Analogue-to-Digital Converter (ADC), which uses integrate-and-fire (I&F) neurons as the encoders of the analogue signal, with modulated inhibitions to decohere the neuronal spikes trains. The architecture consists of an analogue chip and a control module. The analogue chip comprises two scan chains and a two-dimensional integrate-and-fire neuronal array. Individual neurons are accessed via the chains one by one without any encoder decoder or arbiter. The control module is implemented on an FPGA (Field Programmable Gate Array), which sends scan enable signals to the scan chains and controls the inhibition for individual neurons. Since the control module is implemented on an FPGA, it can be easily reconfigured. Additionally, we propose a pulse width modulation methodology for the lateral inhibition, which makes use of different pulse widths indicating different strengths of inhibition for each individual neuron to decohere neuronal spikes. Software simulations in this paper tested the robustness of the proposed ADC architecture to fixed random noise. A circuit simulation using ten neurons shows the performance and the feasibility of the architecture.]]
    Original languageEnglish
    Title of host publicationProceedings of the 2015 Biomedical Circuits and Systems Conference (BioCAS 2015): Engineering for Healthy Minds and Able Bodies: Atlanta, Georgia, USA, October 22-24, 2015
    PublisherIEEE
    Number of pages4
    ISBN (Print)9781479972333
    DOIs
    Publication statusPublished - 2015
    EventBiomedical Circuits and Systems Conference -
    Duration: 22 Oct 2015 → …

    Conference

    ConferenceBiomedical Circuits and Systems Conference
    Period22/10/15 → …

    Keywords

    • neural networks (neurobiology)
    • neuromorphic engineering

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