We held our next tinyML Talks webcast. Masoud Daneshtalab from Mälardalen University presented DeepMaker - Deep Learning Accelerator on Commercial Programmable Devices on June 17, 2021.
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 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.
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Masoud Daneshtalab
Download presentation slides:
Masoud Daneshtalab
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