ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2202.10100
  4. Cited By
Enabling On-Device Smartphone GPU based Training: Lessons Learned

Enabling On-Device Smartphone GPU based Training: Lessons Learned

21 February 2022
Anish Das
Young D. Kwon
Jagmohan Chauhan
Cecilia Mascolo
    3DH
ArXivPDFHTML

Papers citing "Enabling On-Device Smartphone GPU based Training: Lessons Learned"

14 / 14 papers shown
Title
Exploring System Performance of Continual Learning for Mobile and
  Embedded Sensing Applications
Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications
Young D. Kwon
Jagmohan Chauhan
Abhishek Kumar
Pan Hui
Cecilia Mascolo
CLL
HAI
55
33
0
25 Oct 2021
FastICARL: Fast Incremental Classifier and Representation Learning with
  Efficient Budget Allocation in Audio Sensing Applications
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications
Young D. Kwon
Jagmohan Chauhan
Cecilia Mascolo
HAI
59
16
0
14 Jun 2021
Knowing when we do not know: Bayesian continual learning for
  sensing-based analysis tasks
Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasks
S. S. Rodríguez
Cecilia Mascolo
Young D. Kwon
CLL
35
12
0
06 Jun 2021
Permute, Quantize, and Fine-tune: Efficient Compression of Neural
  Networks
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Julieta Martinez
Jashan Shewakramani
Ting Liu
Ioan Andrei Bârsan
Wenyuan Zeng
R. Urtasun
MQ
63
31
0
29 Oct 2020
MNN: A Universal and Efficient Inference Engine
MNN: A Universal and Efficient Inference Engine
Xiaotang Jiang
Huan Wang
Yiliu Chen
Ziqi Wu
Lichuan Wang
...
Zongyang Cui
Yuezhi Cai
Tianhang Yu
Chengfei Lv
Zhihua Wu
74
154
0
27 Feb 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
493
42,407
0
03 Dec 2019
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with
  Edge Computing
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
Zhi Zhou
Xu Chen
En Li
Liekang Zeng
Ke Luo
Junshan Zhang
90
1,440
0
24 May 2019
Low-Memory Neural Network Training: A Technical Report
Low-Memory Neural Network Training: A Technical Report
N. Sohoni
Christopher R. Aberger
Megan Leszczynski
Jian Zhang
Christopher Ré
50
101
0
24 Apr 2019
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture
  Design
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ningning Ma
Xiangyu Zhang
Haitao Zheng
Jian Sun
174
4,987
0
30 Jul 2018
A Systematic DNN Weight Pruning Framework using Alternating Direction
  Method of Multipliers
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
Tianyun Zhang
Shaokai Ye
Kaiqi Zhang
Jian Tang
Wujie Wen
M. Fardad
Yanzhi Wang
57
438
0
10 Apr 2018
MobileNetV2: Inverted Residuals and Linear Bottlenecks
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler
Andrew G. Howard
Menglong Zhu
A. Zhmoginov
Liang-Chieh Chen
181
19,271
0
13 Jan 2018
Quantization and Training of Neural Networks for Efficient
  Integer-Arithmetic-Only Inference
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Benoit Jacob
S. Kligys
Bo Chen
Menglong Zhu
Matthew Tang
Andrew G. Howard
Hartwig Adam
Dmitry Kalenichenko
MQ
150
3,123
0
15 Dec 2017
TensorFlow: A system for large-scale machine learning
TensorFlow: A system for large-scale machine learning
Martín Abadi
P. Barham
Jianmin Chen
Zhiwen Chen
Andy Davis
...
Vijay Vasudevan
Pete Warden
Martin Wicke
Yuan Yu
Xiaoqiang Zhang
GNN
AI4CE
433
18,350
0
27 May 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained
  Quantization and Huffman Coding
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han
Huizi Mao
W. Dally
3DGS
255
8,833
0
01 Oct 2015
1