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Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays
Liquid analysis is key to track conformity with the strict process quality standards of sectors like food, beverage, and chemical manufacturing. In order to analyse product qualities online and at the very point of interest, automated monitoring systems must satisfy strong requirements in terms of m...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8695490/ https://www.ncbi.nlm.nih.gov/pubmed/34955722 http://dx.doi.org/10.3389/fnins.2021.771480 |
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author | LeBow, Nicholas Rueckauer, Bodo Sun, Pengfei Rovira, Meritxell Jiménez-Jorquera, Cecilia Liu, Shih-Chii Margarit-Taulé, Josep Maria |
author_facet | LeBow, Nicholas Rueckauer, Bodo Sun, Pengfei Rovira, Meritxell Jiménez-Jorquera, Cecilia Liu, Shih-Chii Margarit-Taulé, Josep Maria |
author_sort | LeBow, Nicholas |
collection | PubMed |
description | Liquid analysis is key to track conformity with the strict process quality standards of sectors like food, beverage, and chemical manufacturing. In order to analyse product qualities online and at the very point of interest, automated monitoring systems must satisfy strong requirements in terms of miniaturization, energy autonomy, and real time operation. Toward this goal, we present the first implementation of artificial taste running on neuromorphic hardware for continuous edge monitoring applications. We used a solid-state electrochemical microsensor array to acquire multivariate, time-varying chemical measurements, employed temporal filtering to enhance sensor readout dynamics, and deployed a rate-based, deep convolutional spiking neural network to efficiently fuse the electrochemical sensor data. To evaluate performance we created MicroBeTa (Microsensor Beverage Tasting), a new dataset for beverage classification incorporating 7 h of temporal recordings performed over 3 days, including sensor drifts and sensor replacements. Our implementation of artificial taste is 15× more energy efficient on inference tasks than similar convolutional architectures running on other commercial, low power edge-AI inference devices, achieving over 178× lower latencies than the sampling period of the sensor readout, and high accuracy (97%) on a single Intel Loihi neuromorphic research processor included in a USB stick form factor. |
format | Online Article Text |
id | pubmed-8695490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86954902021-12-24 Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays LeBow, Nicholas Rueckauer, Bodo Sun, Pengfei Rovira, Meritxell Jiménez-Jorquera, Cecilia Liu, Shih-Chii Margarit-Taulé, Josep Maria Front Neurosci Neuroscience Liquid analysis is key to track conformity with the strict process quality standards of sectors like food, beverage, and chemical manufacturing. In order to analyse product qualities online and at the very point of interest, automated monitoring systems must satisfy strong requirements in terms of miniaturization, energy autonomy, and real time operation. Toward this goal, we present the first implementation of artificial taste running on neuromorphic hardware for continuous edge monitoring applications. We used a solid-state electrochemical microsensor array to acquire multivariate, time-varying chemical measurements, employed temporal filtering to enhance sensor readout dynamics, and deployed a rate-based, deep convolutional spiking neural network to efficiently fuse the electrochemical sensor data. To evaluate performance we created MicroBeTa (Microsensor Beverage Tasting), a new dataset for beverage classification incorporating 7 h of temporal recordings performed over 3 days, including sensor drifts and sensor replacements. Our implementation of artificial taste is 15× more energy efficient on inference tasks than similar convolutional architectures running on other commercial, low power edge-AI inference devices, achieving over 178× lower latencies than the sampling period of the sensor readout, and high accuracy (97%) on a single Intel Loihi neuromorphic research processor included in a USB stick form factor. Frontiers Media S.A. 2021-12-09 /pmc/articles/PMC8695490/ /pubmed/34955722 http://dx.doi.org/10.3389/fnins.2021.771480 Text en Copyright © 2021 LeBow, Rueckauer, Sun, Rovira, Jiménez-Jorquera, Liu and Margarit-Taulé. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience LeBow, Nicholas Rueckauer, Bodo Sun, Pengfei Rovira, Meritxell Jiménez-Jorquera, Cecilia Liu, Shih-Chii Margarit-Taulé, Josep Maria Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays |
title | Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays |
title_full | Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays |
title_fullStr | Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays |
title_full_unstemmed | Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays |
title_short | Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays |
title_sort | real-time edge neuromorphic tasting from chemical microsensor arrays |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8695490/ https://www.ncbi.nlm.nih.gov/pubmed/34955722 http://dx.doi.org/10.3389/fnins.2021.771480 |
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