Machine Learning for Energy-efficient Manycore Interconnects

Chair

Kun-Chih Chen, National Sun Yat-sen University, Taiwan

Topics of Interest

Computing systems have been an essential factor in the advancement of technology and applications, and machine learning is no different. Large servers have enabled complex machine learning algorithms to achieve high performance in cognitive applications. However, with the rising need for advanced machine learning for large-scale applications, scalable, low-cost, high-performance, and energy-efficient manycore systems on chip (MCSoCs) have become crucial. The topics of interest of the track include, but are not limited to:

  • ML for multicore SoC Design
  • Thermal Design Optimization for 3D-IC
  • ML for Hardware Design
  • IP-obscuring models for non-linear circuits
  • Model-based design with surrogate models
  • Bayesian optimization to find the region of design space

TPC Members

  • Midia Reshadi , Trinity College Dublin, Ireland  
  • Md Farhadur Reza, Eastern Illinois University, USA
  • Poona Bahrebar, Ghent University, Belgium
  • Mayank Parasar, Samsung Austin Research and Development Center, USA
  • Morteza Nabavinejad, Institute for Research in Fundamental Sciences, Iran
  • Mohammad (Amir) Baharloo, Electrical, and Computer Engineering Department, University of Victoria, Canada.

Former Chairs

Amey Kulkrani (NVIDIA, USA), MCSoC 2020