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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SoFA: A Spark-oriented Fog Architecture (Oct 2019) Neda Maleki , Mohammad Loni, Masoud Daneshtalab, Mauro Conti , Hossein Fotouhi IEEE 45th Annual Conference of the Industrial Electronics Society (IECON'19)
Defender: A Low Overhead and Efficient Fault-Tolerant Mechanism for Reliable On-Chip Router (Oct 2019) NAVEED KHAN BALOCH , MUHAMMAD IRAM BAIG , Masoud Daneshtalab Journal of IEEE Access (IEEE-Access)
A Cloud Based Super-Optimization Method to Parallelize the Sequential Code’s Nested Loops (Oct 2019) Amin Majd , Mohammad Loni, Golnaz Sahebi , Masoud Daneshtalab, Elena Troubitsyna IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC-2019)
NeuroPower: Designing Energy Efﬁcient Convolutional Neural Network Architecture for Embedded Systems (Sep 2019) Mohammad Loni, Ali Zoljodi , Sima Sinaei, Masoud Daneshtalab, Mikael Sjödin The 28th International Conference on Artificial Neural Networks (ICANN 2019)
TOT-Net: An Endeavor Toward Optimizing Ternary Neural Networks (Sep 2019) Najmeh Nazari , Mohammad Loni, Mostafa E. Salehi, Masoud Daneshtalab, Mikael Sjödin 22nd Euromicro Conference on Digital System Design (DSD 2019)
An energy-efﬁcient partition-based XYZ-planar routing algorithm for a wireless network-on-chip (Dec 2018) Fahimeh Yazdanpanah , Raheel Afsharmazayejani , Amin Rezaei , Masoud Daneshtalab The Journal of Supercomputing (Supercomputing)