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RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of
  Quantized CNNs

RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of Quantized CNNs

15 January 2023
A. M. Ribeiro-dos-Santos
João Dinis Ferreira
O. Mutlu
G. Falcão
    MQ
ArXivPDFHTML

Papers citing "RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of Quantized CNNs"

5 / 5 papers shown
Title
Forward and Backward Information Retention for Accurate Binary Neural
  Networks
Forward and Backward Information Retention for Accurate Binary Neural Networks
Haotong Qin
Ruihao Gong
Xianglong Liu
Mingzhu Shen
Ziran Wei
F. Yu
Jingkuan Song
MQ
131
324
0
24 Sep 2019
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
950
20,567
0
17 Apr 2017
Incremental Network Quantization: Towards Lossless CNNs with
  Low-Precision Weights
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
Aojun Zhou
Anbang Yao
Yiwen Guo
Lin Xu
Yurong Chen
MQ
319
1,049
0
10 Feb 2017
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
L. V. D. van der Maaten
Kilian Q. Weinberger
PINN
3DV
258
36,371
0
25 Aug 2016
ImageNet Large Scale Visual Recognition Challenge
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLM
ObjD
296
39,198
0
01 Sep 2014
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