Blar i NTNU Open på forfatter "Umuroglu, Yaman"

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    • Accelerating Sparse Linear Algebra and Deep Neural Networks on Reconfigurable Platforms 

      Umuroglu, Yaman (Doctoral theses at NTNU;2018:1, Doctoral thesis, 2018)
      Regardless of whether the chosen figure of merit is execution time, throughput, battery life for an embedded system or total cost of ownership for a datacenter, today’s computers are fundamentally limited by their energy ...

    • BISMO: A Scalable Bit-Serial Matrix Multiplication Overlay for Reconfigurable Computing 

      Umuroglu, Yaman; Rasnayake, Lahiru; Själander, Magnus (Chapter, 2018)
      Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to high-performance implementations. Many ...

    • FINN: A Framework for Fast, Scalable Binarized Neural Network Inference 

      Umuroglu, Yaman; Fraser, Nicholas J.; Gambardella, Giulio; Blott, Michaela; Leong, Philip W.; Jahre, Magnus; Vissers, Kees (Chapter, 2017)
      Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In ...

    • Hardware Acceleration of Convolutional Neural Networks 

      Halvorsen, Magnus (Master thesis, 2015)
      Convolutional neural networks have been widely employed for image recognition applications because of their high accuracy, which they achieve by emulating how our own brain recognizes objects. The possibility of making our ...

    • Mining Bitcoins using a Heterogeneous Computer Architecture 

      Langland, Torbjørn; Skordal, Kristian Klomsten (Master thesis, 2015)
      Recent years have seen the emergence of a new class of currencies, called cryptocurrencies. These currencies use cryptography to provide security and peer-to-peer networking to provide a decentralized system. Bitcoin is the ...

    • Optimizing Bit-Serial Matrix Multiplication for Reconfigurable Computing 

      Umuroglu, Yaman; Davide, Conficconi; Rasnayake, Lahiru; Preusser, Thomas B.; Själander, Magnus (Peer reviewed; Journal article, 2019)
      Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to high-performance implementations. Many ...

    • Scaling Binarized Neural Networks on Reconfigurable Logic 

      Fraser, Nicholas J.; Umuroglu, Yaman; Gambardella, Giulio; Blott, Michaela; Leong, Philip W.; Vissers, Kees; Jahre, Magnus (Chapter, 2017)
      Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suited to reconfigurable logic devices, which ...

    • SHMACsim: A Cycle-accurate Simulation Infrastructure for the Heterogeneous SHMAC Multi-Core Prototype 

      Umuroglu, Yaman (Master thesis, 2013)
      The fast-paced development trend in microprocessor performance characterized by Moore?s Law can no longer continue unperturbed. Shrinking semiconductor node size still translates into increasing transistor count but not ...

    • Turbo Amber: A high-performance processor core for SHMAC 

      Akre, Anders Tvetmarken; Bøe, Sebastian (Master thesis, 2014)
      The performance increase of state of the art processors has stagnated dueto power and thermal constraints. Heterogeneous computing has latelyattracted interest and may be the key for improving the performanceand energy-efficiency ...