Design and Performance Comparison of CNN Accelerators Based on the Intel Movidius Myriad2 SoC and FPGA Embedded Prototype

Kyriakos, Angelos; Papatheofanous, Elissaios Alexios; Bezaitis, Charalampos; Petrongonas, Evangelos; Soudris, Dimitrios; Reisis, D.

doi:10.1109/iccairo47923.2019.00030

Cited by 7 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The practical implementation for the end user of deep learning models is proposed in the work [11], running a convolutional neural network (CNN) trained with the database of numbers (MNIST) on an Intel Movidius Myriad2 card. They use the Tensorflow API to design a lightweight single-convolution CNN.…”

Section: Background and Overviewmentioning

confidence: 99%

Implementing Deep Learning Models in Embedded Systems for Diagnosis Induction Machine

Barrera-Llanga

Sapena-Bañó

Martínez-Román

et al. 2023

IJECER

View full text Add to dashboard Cite

Diagnosis of induction machines based on deep learning models is becoming a trend in maintenance systems of modern industry. The implementation of such systems allows low-cost industrial monitoring due to the characteristics of hardware and software. However, combining deep learning models with current embedded systems is a difficult issue due to the computational cost to run data through neural networks. In particular, balancing the resources of graphics processing unit (GPU) with modern architecture models is a challenge for prototyping induction machine diagnostic systems. In this work, a novel method is proposed to implement trained deep learning models in low-cost embedded systems. We review algorithmic optimizations for running neural network models, balancing computational resources. In addition, we propose the use of a graphical user interface (GUI) as a tool for the end user of the embedded system. The approach proposed in this work can be of great help to future researchers who develop their prototype using deep learning models and low-cost embedded systems.

show abstract

Section: Background and Overviewmentioning

confidence: 99%