ALOHA: An architectural-aware framework for deep learning at the edge

P. Meloni, D. Loi, G. Deriu, O. Ripolles, D. Solans, A. D. Pimentel, D. Sapra, M. Pintor, B. Biggio, B. Moser, N. Shepeleva, T. Stefanov, S. Minakova, F. Conti, L. Benini, N. Fragoulis, I. Theodorakopoulos, M. Masin, F. Palumbo

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Novel Deep Learning (DL) algorithms show ever-increasing accuracy and precision in multiple application domains. However, some steps further are needed towards the ubiquitous adoption of this kind of instrument. First, effort and skills required to develop new DL models, or to adapt existing ones to new use-cases, are hardly available for small- and medium-sized businesses. Second, DL inference must be brought at the edge, to overcome limitations posed by the classically-used cloud computing paradigm. This requires implementation on low-energy computing nodes, often heteroge-nous and parallel, that are usually more complex to program and to manage. This work describes the ALOHA framework, that proposes a solution to these issue by means of an integrated tool ow that automates most phases of the development process. The framework introduces architecture-awareness, considering the target inference platform very early, already during algorithm selection, and driving the optimal porting of the resulting embedded application. Moreover it considers security, power eciency and adaptiveness as main objectives during the whole development process.

Original languageEnglish
Title of host publicationWorkshop Proceedings - 2018 INTelligent Embedded Systems Architectures and Applications, INTESA 2018
PublisherAssociation for Computing Machinery
Pages19-26
Number of pages8
ISBN (Electronic)9781450365987
DOIs
Publication statusPublished - 4 Oct 2018
Externally publishedYes
Event2018 Workshop on INTelligent Embedded Systems Architectures and Applications, INTESA 2018 - Torino, Italy
Duration: 4 Oct 2018 → …

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2018 Workshop on INTelligent Embedded Systems Architectures and Applications, INTESA 2018
CountryItaly
CityTorino
Period4/10/18 → …

Keywords

  • Computer aided design
  • Convolutional Neural Networks
  • Deep Learning

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