High performance accelerators for deep neural networks: A review-Reference-Cited by-同舟云学术

High performance accelerators for deep neural networks: A review

Published:2021-10-11 Issue:1 Volume:39 Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Akhoon Mohd Saqib¹,Suandi Shahrel A.¹,Alshahrani Abdullah²,Saad Abdul‐Malik H. Y.³,Albogamy Fahad R.⁴,Abdullah Mohd Zaid Bin¹,Loan Sajad A.⁵^ORCID

Affiliation:

1. Intelligent Biometric Group, School of Electrical and Electronic Engineering Universiti Sains George Town Malaysia

2. Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering, University of Jeddah Jeddah Saudi Arabia

3. Division of Electronics and Computer Engineering, School of Electrical Engineering, Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia

4. Turabah University College, Computer Sciences Program, Taif University Saudi University Ta'if Saudi Arabia

5. Department of Electronics and Communication Jamia Millia Islamia New Delhi India

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12831

Reference72 articles.

1. YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration

2. NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps

3. Fused-layer CNN accelerators

4. Understanding switching variability and random telegraph noise in resistive RAM

5. Ambrogio S. Narayanan P. Tsai H.&Mackin C.(2020). Accelerating deep neural networks with analog memory devices. In 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE.

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