The Future of Consumer Edge-AI Computing

The Future of Consumer Edge-AI Computing

19 October 2022

Stefanos Laskaridis

Stylianos I. Venieris

Alexandros Kouris

Nicholas D. Lane

Papers citing "The Future of Consumer Edge-AI Computing"

12 / 12 papers shown

Title
MultiTASC++: A Continuously Adaptive Scheduler for Edge-Based Multi-Device Cascade Inference Sokratis Nikolaidis Stylianos I. Venieris I. Venieris 81 0 0 05 Dec 2024
Profiling AI Models: Towards Efficient Computation Offloading in Heterogeneous Edge AI Systems Juan Marcelo Parra Ullauri Oscar Dilley Hari Madhukumar Dimitra Simeonidou 28 2 0 30 Oct 2024
Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework Kexin Zhang Lixin Li Wensheng Lin Yuna Yan Rui Li Wenchi Cheng Zhu Han DiffM 22 4 0 31 Jul 2024
MELTing point: Mobile Evaluation of Language Transformers Stefanos Laskaridis Kleomenis Katevas Lorenzo Minto Hamed Haddadi 27 20 0 19 Mar 2024
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition Samuel Horváth Stefanos Laskaridis Shashank Rajput Hongyi Wang BDL 26 4 0 28 Aug 2023
MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge Sokratis Nikolaidis Stylianos I. Venieris I. Venieris 16 2 0 22 Jun 2023
Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs Alexandros Kouris Stylianos I. Venieris Stefanos Laskaridis Nicholas D. Lane 30 8 0 27 Sep 2022
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning J. Ahn Yeeun Ma Seoyun Park Cheolwoo You FedML 30 21 0 01 Feb 2022
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild Mario Almeida Stefanos Laskaridis Abhinav Mehrotra L. Dudziak Ilias Leontiadis Nicholas D. Lane HAI 95 44 0 28 Sep 2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout Samuel Horváth Stefanos Laskaridis Mario Almeida Ilias Leondiadis Stylianos I. Venieris Nicholas D. Lane 176 267 0 26 Feb 2021
CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo Natalie Dullerud Adam Dziedzic Yunxiang Zhang S. Jha Nicolas Papernot Xiao Wang FedML 59 57 0 09 Feb 2021
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 20,471 0 17 Apr 2017