A Thread-Saving Schedule with Graph Analysis for Parallel Deep Learning Applications on Embedded Systems

2018 ◽  
Author(s):  
Shu Huang ◽  
Jihe Wang ◽  
Danghui Wang ◽  
Meng Zhang ◽  
Hongjun You
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Graph Analysis

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

scholarly journals Pruning Neural Networks Using Multi-Armed Bandits

2019 ◽  
Vol 63 (7) ◽  
pp. 1099-1108
Author(s):  
Salem Ameen ◽  
Sunil Vadera
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Embedded Systems ◽  
Thompson Sampling ◽  
Pruning Methods

Abstract The successful application of deep learning has led to increasing expectations of their use in embedded systems. This, in turn, has created the need to find ways of reducing the size of neural networks. Decreasing the size of a neural network requires deciding which weights should be removed without compromising accuracy, which is analogous to the kind of problems addressed by multi-armed bandits (MABs). Hence, this paper explores the use of MABs for reducing the number of parameters of a neural network. Different MAB algorithms, namely $\epsilon $-greedy, win-stay, lose-shift, UCB1, KL-UCB, BayesUCB, UGapEb, successive rejects and Thompson sampling are evaluated and their performance compared to existing approaches. The results show that MAB pruning methods, especially those based on UCB, outperform other pruning methods.

scholarly journals Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6886
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Embedded Systems ◽  
Image Size ◽  
Bearing Faults ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Machine Health Monitoring ◽  
System Resources

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.

scholarly journals Evaluation Method of Deep Learning-Based Embedded Systems for Traffic Sign Detection

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 101217-101238
Author(s):  
Miguel Lopez-Montiel ◽  
Ulises Orozco-Rosas ◽  
Moises Sanchez-Adame ◽  
Kenia Picos ◽  
Oscar Humberto Montiel Ross
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Evaluation Method ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals CitiusSynapse: A Deep Learning Framework for Embedded Systems

2021 ◽  
Vol 11 (23) ◽  
pp. 11570
Author(s):  
Seungtae Hong ◽  
Hyunwoo Cho ◽  
Jeong-Si Kim
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Parallel Processing ◽  
Embedded Systems ◽  
Structural Characteristics ◽  
Limited Resources ◽  
Learning Framework ◽  
Conventional Methods ◽  
Active Research ◽  
A Performance

As embedded systems, such as smartphones with limited resources, have become increasingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that of standard PCs. The proposed framework supports an OpenCL-based accelerator engine for accelerator deep learning operations in various embedded systems. Moreover, the parallel processing performance of OpenCL is maximized through an OpenCL kernel that is optimized for embedded GPUs, and the structural characteristics of embedded systems, such as unified memory. Furthermore, an on-device optimizer for optimizing the performance in on-device environments, and model converters for compatibility with conventional frameworks, are provided. The results of a performance evaluation show that the proposed on-device framework outperformed conventional methods.

Sign in / Sign up

Export Citation Format

Share Document