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Differentiable Network Pruning for Microcontrollers

Differentiable Network Pruning for Microcontrollers

15 October 2021
Edgar Liberis
Nicholas D. Lane
ArXivPDFHTML

Papers citing "Differentiable Network Pruning for Microcontrollers"

13 / 13 papers shown
Title
DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI
  Accelerators
DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators
Taesik Gong
F. Kawsar
Chulhong Min
64
3
0
09 Dec 2024
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
Xiao Ma
Shengfeng He
Hezhe Qiao
Dong-Lai Ma
29
1
0
14 Mar 2024
Value Prediction for Spatiotemporal Gait Data Using Deep Learning
Value Prediction for Spatiotemporal Gait Data Using Deep Learning
Ryan Cavanagh
Jelena Trajkovic
Wenlu Zhang
I-Hung Khoo
Vennila Krishnan
CVBM
18
0
0
29 Feb 2024
Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables
Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables
Taesik Gong
S. Jang
Utku Günay Acer
F. Kawsar
Chulhong Min
33
2
0
11 Dec 2023
MicroNAS: Memory and Latency Constrained Hardware-Aware Neural
  Architecture Search for Time Series Classification on Microcontrollers
MicroNAS: Memory and Latency Constrained Hardware-Aware Neural Architecture Search for Time Series Classification on Microcontrollers
Tobias King
Yexu Zhou
Tobias Röddiger
Michael Beigl
26
2
0
27 Oct 2023
Cost-Driven Hardware-Software Co-Optimization of Machine Learning
  Pipelines
Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines
Ravit Sharma
W. Romaszkan
Feiqian Zhu
Puneet Gupta
Ankur Mehta
19
0
0
11 Oct 2023
Model Compression in Practice: Lessons Learned from Practitioners
  Creating On-device Machine Learning Experiences
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
Fred Hohman
Mary Beth Kery
Donghao Ren
Dominik Moritz
19
16
0
06 Oct 2023
TinyML: Tools, Applications, Challenges, and Future Research Directions
TinyML: Tools, Applications, Challenges, and Future Research Directions
Rakhee Kallimani
K. Pai
Prasoon Raghuwanshi
S. Iyer
O. López
36
40
0
23 Mar 2023
Pex: Memory-efficient Microcontroller Deep Learning through Partial
  Execution
Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution
Edgar Liberis
Nicholas D. Lane
13
3
0
30 Nov 2022
Machine Learning for Microcontroller-Class Hardware: A Review
Machine Learning for Microcontroller-Class Hardware: A Review
Swapnil Sayan Saha
S. Sandha
Mani B. Srivastava
21
118
0
29 May 2022
Sparsity in Deep Learning: Pruning and growth for efficient inference
  and training in neural networks
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler
Dan Alistarh
Tal Ben-Nun
Nikoli Dryden
Alexandra Peste
MQ
141
684
0
31 Jan 2021
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
R. David
Jared Duke
Advait Jain
Vijay Janapa Reddi
Nat Jeffries
...
Meghna Natraj
Shlomi Regev
Rocky Rhodes
Tiezhen Wang
Pete Warden
107
465
0
17 Oct 2020
What is the State of Neural Network Pruning?
What is the State of Neural Network Pruning?
Davis W. Blalock
Jose Javier Gonzalez Ortiz
Jonathan Frankle
John Guttag
188
1,027
0
06 Mar 2020
1