NN-LUT-Reference-Cited by-同舟云学术

NN-LUT

Published:2022-07-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 59th ACM/IEEE Design Automation Conference
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Author:

Yu Joonsang¹,Park Junki²,Park Seongmin³,Kim Minsoo³,Lee Sihwa³,Lee Dong Hyun²,Choi Jungwook³

Affiliation:

1. NAVER Clova

2. Samsung Advanced Institute of Technology

3. Hanyang University

Funder

Ministry of Trade, Industry Energy (MOTIE, Korea)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3489517.3530505

Reference13 articles.

1. NVIDIA Deep Learning Accelerator. http://nvdla.org/primer.html. NVIDIA Deep Learning Accelerator. http://nvdla.org/primer.html.

2. Optimal Curve Fitting With Piecewise Linear Functions

3. J. Chen and X. Liu. 2017. A high-performance deeply pipelined architecture for elementary transcendental function evaluation. In ICCD. J. Chen and X. Liu. 2017. A high-performance deeply pipelined architecture for elementary transcendental function evaluation. In ICCD.

4. G. Cybenko . 1989. Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems 2, 4 ( 1989 ), 303--314. G. Cybenko. 1989. Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems 2, 4 (1989), 303--314.

5. S. Eldridge F. Raudies D. Zou and A. Joshi. 2014. Neural network-based accelerators for transcendental function approximation. In GLSVLSI. S. Eldridge F. Raudies D. Zou and A. Joshi. 2014. Neural network-based accelerators for transcendental function approximation. In GLSVLSI.

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