Machine Learning Acceleration for Tightly Energy-Constrained Devices

Renzo Andri (Autor)

Qiuting Huang, Andreas Schenk, Mathieu Maurice Luisier, Bernd Witzigmann (Herausgeber)

Buch

XVI, 232 Seiten

2020 | 2020
Hartung-Gorre (Verlag)
978-3-86628-693-1 (ISBN)

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Neural Networks have revolutionized the artificial intelligence and machine learning field in recent years, enabling human and even super-human performance on several challenging tasks in a plethora of different applications. Unfortunately, these networks have dozens of millions of parameters and need billions of complex floating-point operations, which does not fit the requirements of rising Internet-of-Things (IoT) end nodes. In this work, these challenges are tackled on three levels: Efficient design and implementation of embedded hardware, the design of existing low-power microcontrollers and their underlying instruction set architecture, and full-custom hardware accelerator design. Meanwhile, we are investigating novel algorithmic approaches of extreme quantization of neural networks, and analyze their performance and energy efficiency trade-off.

Erscheinungsdatum	19.12.2020
Reihe/Serie	Series in Microelectronics ; 240
Verlagsort	Konstanz
Sprache	englisch
Maße	148 x 210 mm
Gewicht	350 g
Themenwelt	Naturwissenschaften ► Physik / Astronomie ► Elektrodynamik
Themenwelt	Naturwissenschaften ► Physik / Astronomie ► Quantenphysik
Schlagworte	full-custom hardware accelerator design • internet-of-things • low-power microcontrollers • machine learning • Tightly Energy-Constrained Devices
ISBN-10	3-86628-693-7 / 3866286937
ISBN-13	978-3-86628-693-1 / 9783866286931
Zustand	Neuware