Measuring what Really Matters: Optimizing Neural Networks for TinyML

With the surge of inexpensive computational and memory resources, neural networks (NNs) have experienced an unprecedented growth in architectural and computational complexity. Introducing NNs to resource-constrained devices enables cost-efficient deployments, widespread availability, and the preserv...

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Veröffentlicht in:	arXiv.org 2021-04
Hauptverfasser:	Heim, Lennart, Biri, Andreas, Qu, Zhongnan, Thiele, Lothar
Format:	Artikel
Sprache:	eng
Schlagworte:	Architecture Computer architecture Energy consumption Machine learning Network latency Neural networks Optimization Performance measurement
Online-Zugang:	Volltext
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