A Survey of Methods for Low-Power Deep Learning and Computer Vision

A Survey of Methods for Low-Power Deep Learning and Computer Vision

24 March 2020

Yung-Hsiang Lu

George K. Thiruvathukal

Papers citing "A Survey of Methods for Low-Power Deep Learning and Computer Vision"

11 / 11 papers shown

Title
BF-IMNA: A Bit Fluid In-Memory Neural Architecture for Neural Network Acceleration M. Rakka Rachid Karami A. Eltawil M. Fouda Fadi J. Kurdahi MQ 47 1 0 03 Nov 2024
A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission Wenjun Huang Arghavan Rezvani Hanning Chen Yang Ni Sanggeon Yun Sungheon Jeong Mohsen Imani 32 7 0 03 Feb 2024
DSD $^2$ : Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free? Victor Quétu Enzo Tartaglione 32 7 0 02 Mar 2023
Fixing Overconfidence in Dynamic Neural Networks Lassi Meronen Martin Trapp Andrea Pilzer Le Yang Arno Solin BDL 37 16 0 13 Feb 2023
A Survey for Efficient Open Domain Question Answering Qin Zhang Shan Chen Dongkuan Xu Qingqing Cao Xiaojun Chen Trevor Cohn Meng Fang 28 33 0 15 Nov 2022
Comparative Study of Inference Methods for Interpolative Decomposition Jun Lu 14 2 0 29 Jun 2022
Efficient Adaptive Ensembling for Image Classification A. Bruno Davide Moroni M. Martinelli 34 18 0 15 Jun 2022
Bayesian Low-Rank Interpolative Decomposition for Complex Datasets Jun Lu 11 4 0 30 May 2022
Efficient visual object representation using a biologically plausible spike-latency code and winner-take-all inhibition Melani Sanchez-Garcia Tushar Chauhan Benoit R. Cottereau M. Beyeler 11 2 0 20 May 2022
Chemical transformer compression for accelerating both training and inference of molecular modeling Yi Yu K. Börjesson 24 0 0 16 May 2022
Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review Shashank Bangalore Lakshman Nasir U. Eisty 13 12 0 19 Apr 2022