Machine Learning for Energy-efficient Manycore Interconnects


Md. Farhadur Reza, Eastern Illinois University, USA

Computing systems have been an essential factor in advancing technology and applications, and machine learning are 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 system 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