Deep Learning Optimization for Many-Core Virtual Platforms

Hyperspectral Image Classification Using Parallel Autoencoding Diabolo Networks on Multi-Core and Many-Core Architectures

Electronics ◽

10.3390/electronics7120411 ◽

2018 ◽

Vol 7 (12) ◽

pp. 411 ◽

Cited By ~ 4

Author(s):

Emanuele Torti ◽

Alessandro Fontanella ◽

Antonio Plaza ◽

Javier Plaza ◽

Francesco Leporati

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Hyperspectral Image ◽

Unsupervised Classification ◽

Hyperspectral Data ◽

Machine Learning Techniques ◽

High Data ◽

Learning Techniques ◽

Speed Up ◽

Many Core

One of the most important tasks in hyperspectral imaging is the classification of the pixels in the scene in order to produce thematic maps. This problem can be typically solved through machine learning techniques. In particular, deep learning algorithms have emerged in recent years as a suitable methodology to classify hyperspectral data. Moreover, the high dimensionality of hyperspectral data, together with the increasing availability of unlabeled samples, makes deep learning an appealing approach to process and interpret those data. However, the limited number of labeled samples often complicates the exploitation of supervised techniques. Indeed, in order to guarantee a suitable precision, a large number of labeled samples is normally required. This hurdle can be overcome by resorting to unsupervised classification algorithms. In particular, autoencoders can be used to analyze a hyperspectral image using only unlabeled data. However, the high data dimensionality leads to prohibitive training times. In this regard, it is important to realize that the operations involved in autoencoders training are intrinsically parallel. Therefore, in this paper we present an approach that exploits multi-core and many-core devices in order to achieve efficient autoencoders training in hyperspectral imaging applications. Specifically, in this paper, we present new OpenMP and CUDA frameworks for autoencoder training. The obtained results show that the CUDA framework provides a speed-up of about two orders of magnitudes as compared to an optimized serial processing chain.

Download Full-text

Research on Parallel Acceleration for Deep Learning Inference Based on Many-Core ARM Platform

Communications in Computer and Information Science - Advanced Computer Architecture ◽

10.1007/978-981-13-2423-9_3 ◽

2018 ◽

pp. 30-41

Author(s):

Keqian Zhu ◽

Jingfei Jiang

Keyword(s):

Deep Learning ◽

Many Core ◽

Parallel Acceleration

Download Full-text

Layer-Centric Memory Reuse and Data Migration for Extreme-Scale Deep Learning on Many-Core Architectures

ACM Transactions on Architecture and Code Optimization ◽

10.1145/3243904 ◽

2018 ◽

Vol 15 (3) ◽

pp. 1-26 ◽

Cited By ~ 3

Author(s):

Hai Jin ◽

Bo Liu ◽

Wenbin Jiang ◽

Yang Ma ◽

Xuanhua Shi ◽

...

Keyword(s):

Deep Learning ◽

Data Migration ◽

Extreme Scale ◽

Many Core ◽

Memory Reuse

Download Full-text

Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

Download Full-text

Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Download Full-text