Cloud systems:
  • System stack performance optimization of HPC and Cloud systems.
  • Heterogeneous cloud (GPU/FPGA based datacenters) scheduling and orchestration.
  • Serverless computing provider and user side challenges.
    Mobile and IoT systems:
  • Improving energy efficiency of video processing in mobile systems.
  • Hardware-Software co-optimizations in Mobile CPUs.
  • Performance and energy-efficient designs on Internet of Things (IoT) systems.
    Manycore architectures and GPUs:
  • Designing high performance energy-efficient heterogeneous GPU architectures.
  • Compiler-assisted optimization on manycore platforms.
  • Minimizing data movement in Near Data Computing.
    Hardware efficient Machine Learning:
  • Optimizing system hierarchy for emerging Machine Learning and Deep learning applications.
  • Minimizing computations across Deep Learning frameworks for energy efficiency in CPUs.
  • Exploring compute accelerators (GPUs, FPGAs) for Deep Learning application.
    Neuromorphic Architectures:
  • Power-efficient neuromorphic computing systems.
  • Simulation of Spiking neural networks.