1 code implementation • 13 Sep 2023 • Bailey J. Eccles, Philip Rodgers, Peter Kilpatrick, Ivor Spence, Blesson Varghese
Compared to sparse models, the pruned model variants are up to 5. 14x smaller and have a 1. 67x inference latency speedup, with no compromise to sparse model accuracy.
no code implementations • 25 Apr 2022 • Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence, Blesson Varghese
This paper will leverage trade-offs in accuracy, end-to-end latency and downtime for selecting the best technique given user-defined objectives (accuracy, latency and downtime thresholds) when an edge node fails.
1 code implementation • 2 Nov 2021 • Rehmat Ullah, Di wu, Paul Harvey, Peter Kilpatrick, Ivor Spence, Blesson Varghese
Our empirical results on the CIFAR10 dataset, with both balanced and imbalanced data distribution, support our claims that FedFly can reduce training time by up to 33% when a device moves after 50% of the training is completed, and by up to 45% when 90% of the training is completed when compared to state-of-the-art offloading approach in FL.
1 code implementation • 9 Jul 2021 • Di wu, Rehmat Ullah, Paul Harvey, Peter Kilpatrick, Ivor Spence, Blesson Varghese
Further, FedAdapt adopts reinforcement learning based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth.
1 code implementation • 4 Aug 2020 • Francis McNamee, Schahram Dustadar, Peter Kilpatrick, Weisong Shi, Ivor Spence, Blesson Varghese
However, there is limited understanding of: (a) whether and how evolving operational conditions (increased CPU and memory utilization at the edge or reduced data transfer rates between the edge and the cloud) affect the performance of already deployed DNNs, and (b) whether a new partition configuration is required to maximize performance.