DeepMaker aims to provide a framework to generate synthesizable accelerators of Deep Neural Networks (DNNs) that can be used for different FPGA fabrics. DeepMaker enables effective use of DNN acceleration in commercially available devices that can accelerate a wide range of applications without a need of costly FPGA reconﬁgurations.
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ADONN: Adaptive Design of Optimized Deep Neural Networks for Embedded Systems (Aug 2018) Mohammad Loni, Masoud Daneshtalab, Mikael Sjödin 21st Euromicro Conference on Digital System Design (DSD'18)
Using Optimization, Learning, and Drone Reflexes to Maximize Safety of Swarms of Drones (Jul 2018) Amin Majd , Adnan Ashraf , Elena Troubitsyna , Masoud Daneshtalab IEEE Congress on Evolutionary Computation (IEEE CEC)
HoneyWiN: Novel Honeycomb-Based Wireless NoC Architecture in Many-Core Era (May 2018) Raheel Afsharmazayejani , Fahimeh Yazdanpanah , Masoud Daneshtalab International Symposium on Applied Reconfigurable Computing (ARC)
Embedded Acceleration of Image Classification Applications for Stereo Vision Systems (Mar 2018) Mohammad Loni, Carl Ahlberg, Masoud Daneshtalab, Mikael Ekström, Mikael Sjödin Design, Automation & Test in Europe Conference & Exhibition (DATE'18)
A General Methodology on Designing Acyclic Channel Dependency Graphs in Interconnection Networks (Mar 2018) Masoumeh Ebrahimi , Masoud Daneshtalab IEEE MICRO (MICRO)
Parallel imperialist competitive algorithms (Jan 2018) Amin Majd , Golnaz Sahebi , Masoud Daneshtalab, Juha Plosila , Shahriar Lotfi , Hannu Tenhunen Journal of Concurrency and Computation: Practice and Experience (JCC)