Hartung-Gorre Verlag
Inh.: Dr.
Renate Gorre D-78465
Konstanz Fon: +49 (0)7533 97227 Fax: +49 (0)7533 97228 www.hartung-gorre.de
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Series in
Microelectronics
edited by
Qiuting
Huang
Andreas Schenk
Mathieu
Maurice Luisier
Bernd
Witzigmann
Renzo Andri
Machine Learning Acceleration for
Tightly Energy-Constrained Devices
2020. XVI, 232 pages. € 64,00.
ISBN 978-3-86628-693-1
Abstract:
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.
Keywords:
About the Author:
Renzo
Andri was born in Brig-Glis,
Switzerland, in 1990. He received his BSc and MSc in Electrical Engineering from the ETH
Zurich, Switzerland, in 2013 and 2015. From November 2015 to March 2020, he was
pursuing his doctoral studies at the Integrated Systems Laboratory of the ETH
Zurich, and the thesis results are summarized in this book. His research
interests are in low-power acceleration for machine learning applications,
including algorithms, system optimizations, and circuit design. In 2019, Dr. Andri received the IEEE TCAD Donald O. Pederson Award.
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