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

P. Meloni, D. Loi, G. Deriu, A. Pimentel, D. Saprat, M. Pintort, M. Pintort, B. Biggio, O. Ripolles, D. Solans, F. Conti, L. Benini, T. Stefanov, S. Minakova, B. Moser, N. Shepeleva, M. Masin, F. Palumbo, N. Fragoulis, I. Theodorakopoulos. Architecture-aware design and implementation of CNN algorithms for embedded inference: The ALOHA project. pages 52-55, DOI 10.1109/ICM.2018.8704093, 5, 2019.

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
BuchProceedings of 2018 30th International Conference on Microelectronics (ICM 2018)
TypIn Konferenzband
VerlagIEEE
DOI10.1109/ICM.2018.8704093
ISBN978-1-5386-8167-1
Monat5
Jahr2019
Seiten52-55
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.