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Now that I can see, I can improve: Enabling data-driven finetuning of
  CNNs on the edge

Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge

15 June 2020
A. Rajagopal
C. Bouganis
ArXivPDFHTML

Papers citing "Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge"

4 / 4 papers shown
Title
Performance Analysis of DNN Inference/Training with Convolution and
  non-Convolution Operations
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations
H. Esmaeilzadeh
Soroush Ghodrati
A. Kahng
Sean Kinzer
Susmita Dey Manasi
S. Sapatnekar
Zhiang Wang
22
2
0
29 Jun 2023
perf4sight: A toolflow to model CNN training performance on Edge GPUs
perf4sight: A toolflow to model CNN training performance on Edge GPUs
A. Rajagopal
C. Bouganis
23
7
0
12 Aug 2021
It's always personal: Using Early Exits for Efficient On-Device CNN
  Personalisation
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
Ilias Leontiadis
Stefanos Laskaridis
Stylianos I. Venieris
Nicholas D. Lane
65
29
0
02 Feb 2021
Aggregated Residual Transformations for Deep Neural Networks
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie
Ross B. Girshick
Piotr Dollár
Z. Tu
Kaiming He
297
10,225
0
16 Nov 2016
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