Approximating HW Accelerators through Partial Extractions onto Shared Artificial Neural Networks-Reference-Cited by-同舟云学术

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Approximating HW Accelerators through Partial Extractions onto Shared Artificial Neural Networks

Published:2023-01-16 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th Asia and South Pacific Design Automation Conference
language:
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Author:

Chowdhury Prattay¹,Godínez Jorge Castro²,Schafer Benjamin Carrion¹

Affiliation:

1. The University of Texas at Dallas

2. Instituto Tecnológico de Costa Rica, Costa Rica

Funder

NATO

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3566097.3567887

Reference15 articles.

1. Accelerating divergent applications on simd architectures using neural networks;Grigorian B.;ICCD,2014

2. A comparative evaluation of approximate multipliers;Jiang H.;NANOARCH,2016

3. J. Han and M. Orshansky , " Approximate computing: An emerging paradigm for energy-efficient design," in IEEE European Test Symposium (ETS) , May 2013 , pp. 1 -- 6 . J. Han and M. Orshansky, "Approximate computing: An emerging paradigm for energy-efficient design," in IEEE European Test Symposium (ETS), May 2013, pp. 1--6.

4. Accelerating divergent applications on simd architectures using neural networks;Grigorian B.;ICCD,2014

5. Employing classification-based algorithms for general-purpose approximate computing;Oliveira G. F.;DAC,2018

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