Please use this identifier to cite or link to this item:
Title: Embedded Deep Learning Accelerators: A Survey on Recent Advances
Authors: akkad, Ghattas
Mansour, Ali
Inaty, Elie 
Affiliations: Department of Computer Engineering 
Keywords: Computer architecture
Convolutional Neural Network (CNN)
Embedded machine learning
Field programmable gate arrays
Hardware accelerators
Reduced instruction set computing
Issue Date: 2024-05-11
Part of: IEEE Transactions on Artificial Intelligence
Start page: 1
End page: 19
The exponential increase in generated data as well as the advances in high-performance computing has paved the way for the use of complex machine learning methods. Indeed, the availability of Graphical Processing Units (GPU) and Tensor Processing Units (TPU) have made it possible to train and prototype Deep Neural Networks (DNN) on large-scale data sets and for a variety of applications, i.e., vision, robotics, biomedical, etc. The popularity of these DNNs originates from their efficacy and state-of-the-art inference accuracy. However, this is obtained at the cost of a considerably high computational complexity. Such drawbacks rendered their implementation on limited resources, edge devices, without a major loss in inference speed and accuracy, a dire and challenging task. To this extent, it has become extremely important to design innovative architectures and dedicated accelerators to deploy these DNNs to embedded and re-configurable processors in a high-performance low complexity structure. In this study, we present a survey on recent advances in deep learning accelerators (DLA) for heterogeneous systems and Reduced Instruction Set Computer (RISC-V) processors given their open-source nature, accessibility, customizability and universality. After reading this article, the readers should have a comprehensive overview of the recent progress in this domain, cutting edge knowledge of recent embedded machine learning trends and substantial insights for future research directions and challenges.
DOI: 10.1109/TAI.2023.3311776
Type: Journal Article
Appears in Collections:Department of Computer Engineering

Show full item record


checked on Jun 15, 2024

Record view(s)

checked on Jun 15, 2024

Google ScholarTM


Dimensions Altmetric

Dimensions Altmetric

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.