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Disambiguation of "Martinelli, Michele"

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Real-Time Cortical Simulations: Energy and Interconnect Scaling on Distributed Systems.

, , , , , , , , , , , , , and . PDP, page 283-290. IEEE, (2019)

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Dr. Michele Nottoli University of Stuttgart

 

Other publications of authors with the same name

Next generation of Exascale-class systems: ExaNeSt project and the status of its interconnect and storage development., , , , , , , , , and 9 other author(s). Microprocessors and Microsystems - Embedded Hardware Design, (2018)Power, Energy and Speed of Embedded and Server Multi-Cores applied to Distributed Simulation of Spiking Neural Networks: ARM in NVIDIA Tegra vs Intel Xeon quad-cores., , , , , , , , , and . CoRR, (2015)Power-Efficient Computing: Experiences from the COSA Project., , , , , , , , , and 8 other author(s). Scientific Programming, (2017)Real-time cortical simulations: energy and interconnect scaling on distributed systems., , , , , , , , , and 4 other author(s). CoRR, (2018)The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States., , , , , , , , , and 3 other author(s). CoRR, (2018)Dynamic many-process applications on many-tile embedded systems and HPC clusters: The EURETILE programming environment and execution platforms., , , , , , , , , and 11 other author(s). Journal of Systems Architecture - Embedded Systems Design, (2016)The Next Generation of Exascale-Class Systems: The ExaNeSt Project., , , , , , , , , and 8 other author(s). DSD, page 510-515. IEEE Computer Society, (2017)Large Scale Low Power Computing System - Status of Network Design in ExaNeSt and EuroExa Projects., , , , , , , , , and 3 other author(s). PARCO, volume 32 of Advances in Parallel Computing, page 750-759. IOS Press, (2017)Real-Time Cortical Simulations: Energy and Interconnect Scaling on Distributed Systems., , , , , , , , , and 4 other author(s). PDP, page 283-290. IEEE, (2019)Impact of exponential long range and Gaussian short range lateral connectivity on the distributed simulation of neural networks including up to 30 billion synapses., , , , , , , , , and . CoRR, (2015)