Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project
Published in In the proceedings of 2018 30th International Conference on Microelectronics (ICM), 2018
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.
BibTeX:
@conference{8704093,
author = {Meloni, Paolo and Loi, Daniela and Deriu, Gianfranco and Pimentel, Andy D. and Sapra, Dolly and Pintor, Maura and Biggio, Battista and Ripolles, Oscar and Solans, David and Conti, Francesco and Benini, Luca and Stefanov, Todor and Minakova, Svetlana and Moser, Bernhard and Shepeleva, Natalia and Masin, Michael and Palumbo, Francesca and Fragoulis, Nikos and Theodorakopoulos, Ilias},
booktitle = {2018 30th International Conference on Microelectronics (ICM)},
title = {Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project},
year = {2018},
volume = {},
number = {},
url = {https://ieeexplore.ieee.org/document/8704093},
pages = {52-55},
doi = {10.1109/ICM.2018.8704093}
}
Recommended citation: Paolo Meloni, Daniela Loi, Gianfranco Deriu, Andy Pimentel, Dolly Sapra, Maura Pintor, Battista Biggio, Oscar Ripolles, David Solans, Francesco Conti, Luca Benini, Todor Stefanov, Svetlana Minakova, Bernhard Moser, Natalia Shepeleva, Michael Masin, Francesca Palumbo, Nikos Fragoulis, Ilias Theodorakopoulos, "Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project." In the proceedings of 2018 30th International Conference on Microelectronics (ICM), 2018.
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