Machine Learning for Energy-efficient, High-Performance, and Reliable Manycore Systems and Interconnects 

Chair

Md. Farhadur Reza, Eastern Illinois University, USA

Computing systems have been an essential factor in advancing 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, reliable, 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 Manycore System Design and Optimization 
  • ML for Interconnect/NoC Design and Optimization 
  • ML for Thermal Design Optimization for 3D-IC 
  • ML for Scalable modeling of Manycore Systems and Interconnects 
  • ML for Fault Tolerance and Reliability 
  • ML for Hardware-Software Co-design 
  • Resource Management and Allocation in Manycore Systems 
  • Dynamic Workload Management in Manycore Systems 
  • Adaptive Power Management in Manycore Systems 
  • Energy Harvesting for Manycore Systems 
  • Collaborative Learning in Manycore Environments 

Former Chairs

  • Prof. Md. Farhadur RezaEastern Illinois University, USA (16th IEEE MCSoC 2023)