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In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices.
Interference-aware Cooperative Communication in Multi-Radio Multi-Channel Wireless Networks
Kun Xie, Xin Wang, Xueli Liu, Jigang Wen, and Jiannong Cao · 2015
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Towards Deploying Decommissioned Mobile Devices as Cheap Energy-Efficient Compute Nodes
Mohammad Shahrad and David Wentzlaff · 2017
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Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices
Surat Teerapittayanon, Bradley McDanel, et al · 2017
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Attention is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Chameleon: Scalable Adaptation of Video Analytics
Junchen Jiang, Ganesh Ananthanarayanan, Peter Bodik, Siddhartha Sen, and Ion Stoica · 2018
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Learning Differentially Private Recurrent Language Models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
Mario Almeida, Stefanos Laskaridis, Ilias Leontiadis, Stylianos I. Venieris, and Nicholas D. Lane · 2019
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Towards Federated Learning at Scale: System Design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, et al · 2019
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Federated Learning with Autotuned Communication-efficient Secure Aggregation
Keith Bonawitz, Fariborz Salehi, Jakub Konečnỳ, Brendan McMahan, and Marco Gruteser · 2019
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Deep Neural Network Approximation for Custom Hardware: Where we’ve been, where we’re going
Erwei Wang, James J Davis, Ruizhe Zhao, Ho-Cheung Ng, Xinyu Niu, Wayne Luk, Peter YK Cheung, and George A Constantinides · 2019
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All one needs to know about fog computing and related edge computing paradigms: A complete survey
Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, and Jason P Jue · 2019
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Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator
Mohamed S. Abdelfattah, Łukasz Dudziak, Thomas Chau, Royson Lee, Hyeji Kim, and Nicholas D. Lane · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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What should 6g be?
Shuping Dang, Osama Amin, Basem Shihada, and Mohamed-Slim Alouini · 2020
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Cookgan: Meal image synthesis from ingredients
Fangda Han, Ricardo Guerrero, and Vladimir Pavlovic · 2020
Cited alongside, same era.
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Stefanos Laskaridis, Stylianos I. Venieris, Mario Almeida, Ilias Leontiadis, and Nicholas D. Lane · 2020
Cited alongside, same era.
HAPI: Hardware-aware progressive inference
Stefanos Laskaridis, Stylianos I Venieris, Hyeji Kim, and Nicholas D Lane · 2020
Cited alongside, same era.
MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance
Peter Mattson, Vijay Janapa Reddi, Christine Cheng, Cody Coleman, Greg Diamos, David Kanter, Paulius Micikevicius, David Patterson, Guenther Schmuelling, Hanlin Tang, Gu-Yeon Wei, and Carole-Jean Wu · 2020
Cited alongside, same era.
DarkneTZ: Towards Model Privacy at the Edge Using Trusted Execution Environments
Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, et al · 2021
Later among the works it cites.
Tinyml: Current progress, research challenges, and future roadmap
Muhammad Shafique, Theocharis Theocharides, Vijay Janapa Reddy, and Boris Murmann · 2021
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Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
Hongxiang Fan, Thomas Chau, Stylianos I. Venieris, Royson Lee, Alexandros Kouris, Wayne Luk, Nicholas D Lane, and Mohamed S. Abdelfattah · 2022
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Memory-Efficient DNN Training on Mobile Devices
In Gim and JeongGil Ko · 2022
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Imtidad: A reference architecture and a case study on developing distributed ai services for skin disease diagnosis over cloud, fog and edge
Nourah Janbi et al · 2022
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Multi-Exit Semantic Segmentation Networks
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Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, and Hamed Haddadi · 2020
Cited alongside, same era.
BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning
Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu · 2020
Cited alongside, same era.
Smart at What Cost? Characterising Mobile Deep Neural Networks in the Wild
Mario Almeida, Stefanos Laskaridis, Abhinav Mehrotra, Lukasz Dudziak, Ilias Leontiadis, and Nicholas D. Lane · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al · 2021
Cited alongside, same era.
Machine Unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
Cited alongside, same era.
CaPC Learning: Confidential and Private Collaborative Learning
Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, and Xiao Wang · 2021
Cited alongside, same era.
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos I. Venieris, and Nicholas D. Lane · 2021
Cited alongside, same era.
Alexandros Kouris, Stylianos I Venieris, Stefanos Laskaridis, and Nicholas Lane · 2022
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Orchestra: Unsupervised federated learning via globally consistent clustering
Ekdeep Singh Lubana, Chi Ian Tang, Fahim Kawsar, Robert P Dick, and Akhil Mathur · 2022
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The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David Patterson, Joseph Gonzalez, Urs Hölzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R So, Maud Texier, and Jeff Dean · 2022
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The many faces of edge intelligence
Ella Peltonen, Ijaz Ahmad, Atakan Aral, et al · 2022
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Sustainable AI: Environmental Implications, Challenges and Opportunities
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, et al · 2022
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Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
Radosvet Desislavov et al · 2023
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Multiple-deep neural network accelerators for next-generation artificial intelligence systems
Stylianos I. Venieris, Christos-Savvas Bouganis, and Nicholas D. Lane · 2023
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Federated active learning (f-al): An efficient annotation strategy for federated learning
Jin-Hyun Ahn, Yeeun Ma, Seoyun Park, and Cheolwoo You · 2024
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Melting point: Mobile evaluation of language transformers
Stefanos Laskaridis, Kleomenis Kateveas, Lorenzo Minto, and Hamed Haddadi · 2024
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Efficient large language models: A survey
Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, Mosharaf Chowdhury, and Mi Zhang · 2024
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