Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153915
Title: Emotion recognition on edge devices: training and deployment
Authors: Pandelea, Vlad
Ragusa, Edoardo
Apicella, Tommaso
Gastaldo, Paolo
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Pandelea, V., Ragusa, E., Apicella, T., Gastaldo, P. & Cambria, E. (2021). Emotion recognition on edge devices: training and deployment. Sensors, 21(13), 4496-. https://dx.doi.org/10.3390/s21134496
Project: A18A2b0046
Journal: Sensors
Abstract: Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature extractors, and simple hardware-friendly classifiers based on linear separators can achieve competitive performance while allowing real-time inference and fast training. Various solutions including batch and Online Sequential Learning are analyzed. Additionally, our experiments show that latency and performance can be further improved via dimensionality reduction and pre-training, respectively. The resulting system is implemented on two types of edge device, namely an edge accelerator and two smartphones.
URI: https://hdl.handle.net/10356/153915
ISSN: 1424-8220
DOI: 10.3390/s21134496
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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