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Six TrueNorth Algorithms & Applications by IBM and Partners at WCCI

August 3, 2016 By dmodha

Guest Blog by Andrew Cassidy and Michael Debole

At the IEEE 2016 World Congress on Computational Intelligence (WCCI) in Vancouver Canada last week, six researchers presented their research on TrueNorth-based algorithms and applications. These papers, published in the proceedings of the International Joint Conference on Neural Networks (IJCNN 2016), represent early outcomes from university and government research collaborators, who were among the first adopters of the TrueNorth hardware and software ecosystem. These research partners were trained at the Brain-inspired Boot Camp last August 2015, and submitted their succeeding research for conference review in January 2016.

The six papers presented at the Special Session on Energy-Efficient Deep Neural Networks were:

  • LATTE: Low-power Audio Transform with TrueNorth Ecosystem.
    Wei-Yu Tsai, Davis Barch, Andrew Cassidy, Michael DeBole, Alexander Andreopoulos, Bryan Jackson, Myron Flickner, Dharmendra Modha, Jack Sampson and Vijaykrishnan Narayanan;
    The Pennsylvania State University, IBM Research – Almaden.
  • TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth.
    Peter U. Diehl, Bruno U. Pedroni, Andrew Cassidy, Paul Merolla, Emre Neftci and Guido Zarrella;
    ETH Zurich, UC San Diego, IBM Research – Almaden, UC Irvine, The MITRE Corporation.
  • Probabilistic Inference Using Stochastic Spiking Neural Networks on A Neurosynaptic Processor.
    Khadeer Ahmed, Amar Shrestha, Qinru Qiu and Qing Wu;
    Syracuse University, Air Force Research Laboratory.
  • Weighted Population Code for Low Power Neuromorphic Image Classification.
    Antonio Jimeno Yepes, Jianbin Tang, Shreya Saxena, Tobias Brosch and Arnon Amir;
    IBM Research – Australia, Massachusetts Institute of Technology, IBM Research –
    Almaden, Institute of Neural Information Processing – Ulm University.
  • Sparse Approximation on Energy Efficient Hardware.
    Kaitlin Fair and David Anderson;
    Georgia Institute of Technology.
  • A Low-Power Neurosynaptic Implementation of Local Binary Patterns for Texture Analysis.
    Alexander Andreopoulos, Rodrigo Alvarez-Icaza, Andrew Cassidy and Myron Flickner;
    IBM Research – Almaden.

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Filed Under: Brain-inspired Computing, Collaborations

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