Structure of Deep Learning Inference Engines for Embedded Systems

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.

Download Full-text

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

2018 IEEE International Conference on Smart Cloud (SmartCloud) ◽

10.1109/smartcloud.2018.00026 ◽

2018 ◽

Author(s):

Shu Huang ◽

Jihe Wang ◽

Danghui Wang ◽

Meng Zhang ◽

Hongjun You

Keyword(s):

Deep Learning ◽

Embedded Systems ◽

Graph Analysis

Download Full-text

Real-Time Tomato Detection, Classification, and Counting System Using Deep Learning and Embedded Systems

Advances in Intelligent Systems and Computing - Proceedings of the International e-Conference on Intelligent Systems and Signal Processing ◽

10.1007/978-981-16-2123-9_39 ◽

2021 ◽

pp. 511-522

Author(s):

Stavan Ruparelia ◽

Monil Jethva ◽

Ruchi Gajjar

Keyword(s):

Deep Learning ◽

Embedded Systems ◽

Real Time ◽

Counting System ◽

Tomato Detection

Download Full-text

Real-time task scheduling and network device security for complex embedded systems based on deep learning networks

Microprocessors and Microsystems ◽

10.1016/j.micpro.2020.103282 ◽

2020 ◽

Vol 79 ◽

pp. 103282 ◽

Cited By ~ 12

Author(s):

Junyan Zhou

Keyword(s):

Deep Learning ◽

Embedded Systems ◽

Real Time ◽

Task Scheduling ◽

Learning Networks ◽

Time Task ◽

Network Device

Download Full-text

Pruning Neural Networks Using Multi-Armed Bandits

The Computer Journal ◽

10.1093/comjnl/bxz078 ◽

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.

Download Full-text

Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems

Sensors ◽

10.3390/s20236886 ◽

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.

Download Full-text