Energy- and Quality-Efficient Approximate Multipliers for Neural Network and Image Processing Applications-Reference-Cited by-同舟云学术

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Energy- and Quality-Efficient Approximate Multipliers for Neural Network and Image Processing Applications

Published:2021 Issue: Volume: Page:1-1
ISSN:2168-6750
Container-title:IEEE Transactions on Emerging Topics in Computing
language:
Short-container-title:IEEE Trans. Emerg. Topics Comput.

Author:

Ahmadinejad Mohammad¹,Moaiyeri Mohammad Hosein²

Affiliation:

1. Electrical Engineering, Shahid Beheshti University, 48512 Tehran, Tehran, Iran (the Islamic Republic of), (e-mail: mo.ahmadinejad@mail.sbu.ac.ir)

2. Electrical Engineering Dep., Shahid Beheshti University, 48512 Tehran, Tehran, Iran (the Islamic Republic of), (e-mail: h_moaiyeri@sbu.ac.ir)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Human-Computer Interaction,Information Systems,Computer Science (miscellaneous)

Link

http://xplorestaging.ieee.org/ielx7/6245516/6558478/09403977.pdf?arnumber=9403977

Cited by 40 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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2. Hybrid Radix-16 booth encoding and rounding-based approximate Karatsuba multiplier for fast Fourier transform computation in biomedical signal processing application;Integration;2024-09

3. Energy-Efficient Neural Network Acceleration Using Most Significant Bit-Guided Approximate Multiplier;Electronics;2024-08-01

4. Two energy efficient unsigned approximate multipliers with N-4 compressors;IEICE Electronics Express;2024-07-25

5. Iterative construction of energy and quality-efficient approximate multipliers utilizing lower bit-length counterparts;The Journal of Supercomputing;2024-05-26

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