Architecture-aware design and implementation of CNN algorithms for embedded inference: The ALOHA project

Autoren Paolo Meloni
Daniela Loi
Gianfranco Deriu
Andy D. Pimentel
Dolly Saprat
Maura Pintort
Maura Pintort
Battista Biggio
Oscar Ripolles
David Solans
Francesco Conti
Luca Benini
Todor Stefanov
Svetlana Minakova
Bernhard A. Moser
Natalia Shepeleva
Michael Masin
Francesca Palumbo
Nikos Fragoulis
Ilias Theodorakopoulos
Editoren
Titel Architecture-aware design and implementation of CNN algorithms for embedded inference: The ALOHA project
Buchtitel Proceedings of 2018 30th International Conference on Microelectronics (ICM 2018)
Typ in Konferenzband
Verlag IEEE
ISBN 978-1-5386-8167-1
DOI 10.1109/ICM.2018.8704093
Monat May
Jahr 2019
Seiten 52-55
SCCH ID# 18080
Abstract

The use of Deep Learning (DL) algorithms is increasingly evolving in many application domains. Despite the rapid growing of algorithm size and complexity, performing DL inference at the edge is becoming a clear trend to cope with low latency, privacy and bandwidth constraints. Nevertheless, traditional implementation on low-energy computing nodes often requires experience-based manual intervention and trial-and-error iterations to get to a functional and effective solution. This work presents a computer-aided design (CAD) support for effective implementation of DL algorithms on embedded systems, aiming at automating different design steps and reducing cost. The proposed tool flow comprises capabilities to consider architecture-and hardware-related variables at very early stages of the development process, from pre-training hyperparameter optimization and algorithm configuration to deployment, and to adequately address security, power efficiency and adaptivity requirements. This paper also presents some preliminary results obtained by the first implementation of the optimization techniques supported by the tool flow.