| SPINN: synergistic progressive inference of neural networks over device and cloud S Laskaridis, SI Venieris, M Almeida, I Leontiadis, ND Lane Proceedings of the 26th annual international conference on mobile computing …, 2020 | 315 | 2020 |
| Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout S Horvath, S Laskaridis, M Almeida, I Leontiadis, S Venieris, N Lane Advances in Neural Information Processing Systems 34, 12876-12889, 2021 | 287 | 2021 |
| Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions S Laskaridis, A Kouris, ND Lane Proceedings of the 5th International Workshop on Embedded and Mobile Deep …, 2021 | 124 | 2021 |
| EmBench: Quantifying performance variations of deep neural networks across modern commodity devices M Almeida, S Laskaridis, I Leontiadis, SI Venieris, ND Lane The 3rd international workshop on deep learning for mobile systems and …, 2019 | 82 | 2019 |
| HAPI: Hardware-aware progressive inference S Laskaridis, SI Venieris, H Kim, ND Lane Proceedings of the 39th International Conference on Computer-Aided Design, 1-9, 2020 | 57 | 2020 |
| Smart at what cost? characterising mobile deep neural networks in the wild M Almeida, S Laskaridis, A Mehrotra, L Dudziak, I Leontiadis, ND Lane Proceedings of the 21st ACM Internet Measurement Conference, 658-672, 2021 | 48 | 2021 |
| Multi-exit semantic segmentation networks A Kouris, SI Venieris, S Laskaridis, N Lane European Conference on Computer Vision, 330-349, 2022 | 42 | 2022 |
| Dyno: Dynamic onloading of deep neural networks from cloud to device M Almeida, S Laskaridis, SI Venieris, I Leontiadis, ND Lane ACM Transactions on Embedded Computing Systems 21 (6), 1-24, 2022 | 38 | 2022 |
| Shrinkml: End-to-end asr model compression using reinforcement learning Ł Dudziak, MS Abdelfattah, R Vipperla, S Laskaridis, ND Lane INTERSPEECH 2019, 2019 | 30 | 2019 |
| It's always personal: Using early exits for efficient on-device CNN personalisation I Leontiadis, S Laskaridis, SI Venieris, ND Lane Proceedings of the 22nd International Workshop on Mobile Computing Systems …, 2021 | 29 | 2021 |
| MELTing point: Mobile Evaluation of Language Transformers S Laskaridis, K Katevas, L Minto, H Haddadi International Conference on Mobile Computing and Networking (MobiCom'24), 2024 | 13 | 2024 |
| The future of consumer edge-ai computing S Laskaridis, SI Venieris, A Kouris, R Li, ND Lane IEEE Pervasive Computing, 2024 | 12 | 2024 |
| Fluid batching: Exit-aware preemptive serving of early-exit neural networks on edge npus A Kouris, SI Venieris, S Laskaridis, ND Lane International Conference on Computer-Aided Design (ICCAD'23), 2022 | 10 | 2022 |
| Fedoras: Federated architecture search under system heterogeneity L Dudziak, S Laskaridis, J Fernandez-Marques Workshop on Federated Learning: Recent Advances and New Challenges (in …, 2022 | 10 | 2022 |
| Federated learning for inference at anytime and anywhere Z Liu, D Li, J Fernandez-Marques, S Laskaridis, Y Gao, Ł Dudziak, SZ Li, ... arXiv preprint arXiv:2212.04084, 2022 | 8 | 2022 |
| Federated mobile sensing for activity recognition S Laskaridis, D Spathis, M Almeida Proceedings of the 27th Annual International Conference on Mobile Computing …, 2021 | 4 | 2021 |
| Maestro: Uncovering Low-Rank Structures via Trainable Decomposition S Horváth, S Laskaridis, S Rajput, H Wang International Conference on Machine Learning (ICML'24), 2023 | 3 | 2023 |
| Cross-device Federated Architecture Search S Laskaridis, J Fernandez-Marques, Ł Dudziak Workshop on Federated Learning: Recent Advances and New Challenges (in …, 2022 | 3 | 2022 |
| Method and system for neural network execution distribution M Almeida, S Laskaridis, S Venieris, I Leontiadis US Patent App. 17/420,259, 2022 | 3 | 2022 |
| Method and system for implementing a variable accuracy neural network S Laskaridis, H Kim, S Venieris US Patent App. 16/923,447, 2021 | 3 | 2021 |
Nicholas LaneUniversity of Cambridge and Flower LabsDirección de correo verificada de cam.ac.uk
