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

Autoren P. Meloni
D. Loi
G. Deriu
A. D. Pimentel
D. Sapra
Bernhard A. Moser
Natalia Shepeleva
F. Conti
L. Benini
O. Ripolles
D. Solans
M. Pintor
B. Biggio
T. Stefanov
S. Minakova
N. Fragoulis
I. Theodorakopoulos
M. Masin
F. Palumbo
Titel ALOHA: An architectural-aware framework for deep learning at the edge
Buchtitel roceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications (INTESA'18)
Typ in Konferenzband
Verlag ACM
ISBN 978-1-4503-6598-7
DOI 10.1145/3285017.3285019
Monat October
Jahr 2018
Seiten 19-26
SCCH ID# 18076

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 heterogenous 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 flow 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 efficiency and adaptiveness as main objectives during the whole development process.