Wildfires: Fire expert urges Australian government to “prepare now for 2021”
Podcast: Drug policy, Australian megafires and London fatbergs
Earth’s oldest known impact might have ended ‘snowball Earth’ ice age
Welcome to the Intelligent Digital Systems Lab at Imperial College
The iDSL lab is part of the Electrical and Electronic Engineering Department of Imperial College London.
Welcome to the Intelligent Digital Systems Lab webpage.
Dr.Bouganis introduces 12-year students to our work on Convolutional Neural Networks.
We had a great time demonstrating our drone research in this year's Imperial Festival.
fpgaConvNet is a framework developed in our lab to bridge the gap between DL developers and FPGAs
"Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions" accepted on ACM Computing Surveys (Click to access the proposed CNN-to-FPGA Benchmark Suite).
We collaborate with KIOS Research Center on enhancing drones with Embedded Visual Intelligence.
"CascadeCNN: Pushing the performance limits of CNN quantisation" accepted on FPL'2018 (Preprint to be released soon).
Stylianos presenting an overview of our work in Deployment of DNNs in the Embedded Space. (slides)
Selected Publications: [Early Access] IEEE Transactions on Neural Networks and Learning Systems, 2018
Selected Publications: [Preprint] IROS 2019
Selected Publications: ISVLSI 2019
Selected Publications: [Preprint] ACM Computing Surveys (2018)
Selected Publications: [Preprint] IROS 2018
Selected Publications: ARC 2019
Selected Publications: [Preprint] FPL 2018
Selected Publications: [Extended Abstract] SysML 2018
Selected Publications: [Preprint] ARC 2018
Selected Publications: FPL 2017
Selected Publications: FCCM 2016
Selected Publications: FCCM 2017
Selected Publications: LearningSys 2015
Selected Publications: IEEE Transactions on Circuits and Systems for Video Technology 2014
In the Intelligent Digital Systems Lab, we perform research towards high-performance (embedded) digital systems spanning several topic areas, including machine learning, computer vision, and robotics.