Inh.: Dr. Renate Gorre
Fon: +49 (0)7533 97227
Fax: +49 (0)7533 97228
Series in Microelectronics
edited by Qiuting Huang
Mathieu Maurice Luisier
Machine Learning Acceleration for
Tightly Energy-Constrained Devices
2020. XVI, 232 pages. € 64,00.
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.
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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