Schedule
Timezone: PDT
DeepMaker – Deep Learning Accelerator on Commercial Programmable Devices
Masoud DANESHTALAB, Professor
Mälardalen University
Currently, the use of deep neural networks (DNN) in industrial systems is gaining significant momentum. However, optimization and deployment of DNN architectures for resource-constrained embedded programmable systems is a relatively costly process. This talk will introduce the DeepMaker framework that optimizes the DNN architectures for resource-constrained edge devices.
Masoud DANESHTALAB, Professor
Mälardalen University
Masoud Daneshtalab is professor at Mälardalen University (MDH) in
Sweden and an adjunct Professor at Tallinn University of Technology
(TalTech) in Estonia. He is co-leading the Heterogeneous System
research group (www.es.mdh.se/hero/). Since 2016 he has been in the Euromicro board of Directors, a faculty member of the HiPEAC network, and a permanent associate editor of Elsevier MICPRO. His research interests include hardware/software co-design and deep learning acceleration.
He has published 2 books and over 200 refereed international journals
and conference papers.
Schedule subject to change without notice.