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Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems

Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems

Pietro Farina
Subrata Biswas
Eren Yildiz
Khakim Akhunov
Saad Ahmed
Bashima Islam
K. Yıldırım
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Papers citing "Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems"

5 / 5 papers shown
Title
Edge Impulse: An MLOps Platform for Tiny Machine Learning
Edge Impulse: An MLOps Platform for Tiny Machine Learning
Shawn Hymel
Colby R. Banbury
Daniel Situnayake
A. Elium
Carl Ward
...
Louis Moreau
Dmitry Maslov
A. Beavis
Jan Jongboom
Vijay Janapa Reddi
VLM
LRM
66
98
0
02 Nov 2022
MCUNet: Tiny Deep Learning on IoT Devices
MCUNet: Tiny Deep Learning on IoT Devices
Ji Lin
Wei-Ming Chen
Chengyue Wu
J. Cohn
Chuang Gan
Song Han
138
487
0
20 Jul 2020
Rethinking the Value of Network Pruning
Rethinking the Value of Network Pruning
Zhuang Liu
Mingjie Sun
Tinghui Zhou
Gao Huang
Trevor Darrell
36
1,471
0
11 Oct 2018
Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems
Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems
Graham Gobieski
Nathan Beckmann
Brandon Lucia
41
204
0
28 Sep 2018
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
237
8,821
0
01 Oct 2015

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