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Neuromorphic object localization using resistive memories and ultrasonic transducers

Author

Listed:
  • Filippo Moro

    (Université Grenoble Alpes)

  • Emmanuel Hardy

    (Université Grenoble Alpes)

  • Bruno Fain

    (Université Grenoble Alpes)

  • Thomas Dalgaty

    (Université Grenoble Alpes
    Université Grenoble Alpes)

  • Paul Clémençon

    (Université Grenoble Alpes
    Université de Tours)

  • Alessio Prà

    (Université Grenoble Alpes
    Università degli Studi di Udine)

  • Eduardo Esmanhotto

    (Université Grenoble Alpes)

  • Niccolò Castellani

    (Université Grenoble Alpes)

  • François Blard

    (Université Grenoble Alpes)

  • François Gardien

    (Université Grenoble Alpes)

  • Thomas Mesquida

    (Université Grenoble Alpes)

  • François Rummens

    (Université Grenoble Alpes)

  • David Esseni

    (Università degli Studi di Udine)

  • Jérôme Casas

    (Université de Tours)

  • Giacomo Indiveri

    (University of Zürich and ETH Zürich)

  • Melika Payvand

    (University of Zürich and ETH Zürich)

  • Elisa Vianello

    (Université Grenoble Alpes)

Abstract

Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary Metal-Oxide Semiconductor neuromorphic architectures provide an ideal hardware substrate for such tasks. To demonstrate the full potential of such systems, we propose and experimentally demonstrate an end-to-end sensory processing solution for a real-world object localization application. Drawing inspiration from the barn owl’s neuroanatomy, we developed a bio-inspired, event-driven object localization system that couples state-of-the-art piezoelectric micromachined ultrasound transducer sensors to a neuromorphic resistive memories-based computational map. We present measurement results from the fabricated system comprising resistive memories-based coincidence detectors, delay line circuits, and a full-custom ultrasound sensor. We use these experimental results to calibrate our system-level simulations. These simulations are then used to estimate the angular resolution and energy efficiency of the object localization model. The results reveal the potential of our approach, evaluated in orders of magnitude greater energy efficiency than a microcontroller performing the same task.

Suggested Citation

  • Filippo Moro & Emmanuel Hardy & Bruno Fain & Thomas Dalgaty & Paul Clémençon & Alessio Prà & Eduardo Esmanhotto & Niccolò Castellani & François Blard & François Gardien & Thomas Mesquida & François Ru, 2022. "Neuromorphic object localization using resistive memories and ultrasonic transducers," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31157-y
    DOI: 10.1038/s41467-022-31157-y
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    References listed on IDEAS

    as
    1. Kaushik Roy & Akhilesh Jaiswal & Priyadarshini Panda, 2019. "Towards spike-based machine intelligence with neuromorphic computing," Nature, Nature, vol. 575(7784), pages 607-617, November.
    2. Stefano Ambrogio & Pritish Narayanan & Hsinyu Tsai & Robert M. Shelby & Irem Boybat & Carmelo Nolfo & Severin Sidler & Massimo Giordano & Martina Bodini & Nathan C. P. Farinha & Benjamin Killeen & Chr, 2018. "Equivalent-accuracy accelerated neural-network training using analogue memory," Nature, Nature, vol. 558(7708), pages 60-67, June.
    3. Bin Gao & Ying Zhou & Qingtian Zhang & Shuanglin Zhang & Peng Yao & Yue Xi & Qi Liu & Meiran Zhao & Wenqiang Zhang & Zhengwu Liu & Xinyi Li & Jianshi Tang & He Qian & Huaqiang Wu, 2022. "Memristor-based analogue computing for brain-inspired sound localization with in situ training," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    4. M. Prezioso & F. Merrikh-Bayat & B. D. Hoskins & G. C. Adam & K. K. Likharev & D. B. Strukov, 2015. "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, Nature, vol. 521(7550), pages 61-64, May.
    5. Nicol S. Harper & David McAlpine, 2004. "Optimal neural population coding of an auditory spatial cue," Nature, Nature, vol. 430(7000), pages 682-686, August.
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    Cited by:

    1. Simone D’Agostino & Filippo Moro & Tristan Torchet & Yiğit Demirağ & Laurent Grenouillet & Niccolò Castellani & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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