Sökning: "massively parallel simulations"

Visar resultat 11 - 13 av 13 avhandlingar innehållade orden massively parallel simulations.

  1. 11. Large-scale simulation of neuronal systems

    Författare :Mikael Djurfeldt; Örjan Ekeberg; Charles Peck; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Computer science; Datavetenskap;

    Sammanfattning : Biologically detailed computational models of large-scale neuronal networks have now become feasible due to the development of increasingly powerful massively parallel supercomputers. We report here about the methodology involved in simulation of very large neuronal networks. LÄS MER

  3. 12. Internal Cooling Design Using Multiphysics Topology Optimization

    Författare :Jonas Lundgren; Carl-Johan Thore; Anders Klarbring; Jan-Erik Lundgren; Joe Alexandersen; Linköpings universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY;

    Sammanfattning : This thesis investigates topology optimization (TO) as a tool for designing optimal internal cooling configurations in components subjected to external hot gas streams. The work is motivated by the challenge of simultaneously considering objectives from multiple physics domains, and the rapid development of additive manufacturing (AM) in the industry, which makes it possible to realize highly complex TO designs. LÄS MER

  4. 13. Event-Driven Architectures for Heterogeneous Neuromorphic Computing Systems

    Författare :Mattias Nilsson; Fredrik Sandin; Foteini Liwicki; Jerker Delsing; Chiara Bartolozzi; Luleå tekniska universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Neuromorphic computing; Mixed-signal; Low-power; Non-von Neumann; Spatiotemporal pattern recognition; System integration; Cyber-Physical Systems; Cyberfysiska system;

    Sammanfattning : Mixed-signal neuromorphic processors have brain-like organization and device physics optimized for emulation of spiking neural networks (SNNs), and offer an energy-efficient alternative for implementing artificial intelligence in applications where deep learning based on conventional digital computing is unfeasible or unsustainable. However, efficient use of such hardware requires appropriate configuration of its inhomogeneous, analog neurosynaptic circuits, with methods for sparse, spike-timing-based information encoding and processing. LÄS MER