Neuromorphic object localization using resistive memories and ultrasonic transducers

Filippo Moro (), Emmanuel Hardy, Bruno Fain, Thomas Dalgaty, Paul Clémençon, Alessio Prà, Eduardo Esmanhotto, Niccolò Castellani, François Blard, François Gardien, Thomas Mesquida, François Rummens, David Esseni, Jérôme Casas, Giacomo Indiveri, Melika Payvand and Elisa Vianello ()
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Filippo Moro: Université Grenoble Alpes
Emmanuel Hardy: Université Grenoble Alpes
Bruno Fain: Université Grenoble Alpes
Thomas Dalgaty: Université Grenoble Alpes
Paul Clémençon: Université Grenoble Alpes
Alessio Prà: Université Grenoble Alpes
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

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-31157-y

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