DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs

The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applications. Low-Cost MCU-based end-nodes have limited on-chip memory and often replace caches with scratchpads, to reduce area...

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Vydané v:	IEEE transactions on computers Ročník 70; číslo 8; s. 1253 - 1268
Hlavní autori:	Burrello, Alessio, Garofalo, Angelo, Bruschi, Nazareno, Tagliavini, Giuseppe, Rossi, Davide, Conti, Francesco
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Acceleration Artificial neural networks C (programming language) Chips (memory devices) Computer architecture Deep neural networks DNN acceleration edge computing Internet of Things IoT Low cost Machine learning Memory management Micromechanical devices Nodes Open source software Software Source code Static random access memory System-on-chip Task analysis Tiling Topology
ISSN:	0018-9340, 1557-9956
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