The computing everywhere paradigm is paving the way for the pervasive diffusion of tiny devices (such as Internet-of-Things or edge computing devices) endowed with intelligent abilities. Achieving this goal requires machine and deep learning solutions to be completely redesigned to fit the severe technological constraints on computation, memory, and power consumption typically characterizing these tiny devices. The aim of this paper is to explore tiny machine learning (TinyML) and introduce tiny deep learning (TinyDL) for the design, development, and deployment of machine and deep learning solutions for (an ecosystem of) tiny devices, hence supporting intelligent and pervasive applications following the computing everywhere paradigm.
CITATION STYLE
Roveri, M. (2023). Is Tiny Deep Learning the New Deep Learning? In Lecture Notes on Data Engineering and Communications Technologies (Vol. 142, pp. 23–39). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-3391-2_2
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