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A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data by Anup Vanarse 1,*, Adam Osseiran 1, Alexander Rassau 1 and Peter van der Made 21School of Engineering, Edith Cowan University, 6027 Perth, Australia2Brainchip Inc., Aliso Viejo, CA 92656, USA*Author to whom correspondence should be addressed. Sensors 2019, 19(22), 4831; https://doi.org/10.3390/s19224831Received: 4 October 2019 / Revised: 26 October 2019 / Accepted: 31 October 2019 / Published: 6 November 2019(This article belongs to the Special Issue Advances in Biomimetic Olfactory Sensors and Electronic Noses and Their Applications)View Full-Text Download PDF Browse FiguresAbstractIn several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking neural network (SNN)-based classifier, implemented in a chip-emulation-based development environment, that can be seamlessly deployed on a neuromorphic system-on-a-chip (NSoC). Under three different scenarios of increasing complexity, the SNN was determined to be able to classify real-valued sensor data with greater than 90% accuracy and with a maximum latency of 3 s on the software-based platform. Highlights of this work included the design and implementation of a novel encoder for artificial olfactory systems, implementation of unsupervised spike-timing-dependent plasticity (STDP) for learning, and a foundational study on early classification capability using the SNN-based classifier. View Full-TextKeywords: SNN-based classification; neuromorphic olfaction; bio-inspired electronic nose systems