Research Group

Do checkout our group page: SPARK (Systems, Parallelization and Architecture Research at NITK) lab at NITK.

Research Scholars

  • Anil Kumar - Machine Learning, Network On Chips.
  • Bheemappa Halavar - 3D Network On Chips.
  • Khyamling Parane - Network On Chip,FPGA Acceleration
  • Prabhu Prasad - Network On Chip, FPGA Acceleration.
  • Pramod Yelmewad - High Performance Computing.
  • Kallinatha H D

Research at SPARK

As Moore's law continues to prevail, more cores and components have been crammed onto a single die. The surge in on-chip communication has lead to a structured hardware communication framework - the Network-on-Chip. Our group focusses on various issues in this challenging and exciting area.

Machine Learning based Performance and Power prediction of NoCs

Performance accurate software simulators are generally too slow for interactive use. We are building a machine learning framework that uses existing results of the simulators (Booksim 2.0 and Orion) and predicts the overall performance and power of the NoC. Such a framework can potentially save simulation time while evaluating options from a large design space.

Researcher(s): Anil Kumar

Design of an Efficient 3D-NoC Architecture for Modern Processors

NoCs on 3D ICs technology provides an opportunity to better the on chip communication delay, energy and area parameters compared to the 2D-NoCs. We are extending the existing simulators to support 3D-NoC topologies. Design space exploration of 3D-NoC is being driven by considering physical characteristics of vertical connections like Through Silicon Vias (TSVs). The exploration is aided using power, performance and cost metrics such as area, throughput, avg. flit latency, Energy per bit transferred, and EDP.

Researcher(s): Bheemappa Halavar

On-Chip Network simulation acceleration using FPGA

NoC researchers have relied on cycle accurate power and performance simulators (viz. Orion, Garnet, Noxim, SICOSYS, Booksim) to explore the micro-architectural design space of on-chip networks. The NoC parameters such as topology, routing algorithm, flow control, and router micro-architecture, including buffer management and allocation schemes can be analyzed using these simulators. Large scale design space exploration of NoCs can be very time-intensive. To address this issue we propose hardware based acceleration using FPGA to speed up the NoC simulation. Fast and accurate simulators provide a vehicle for the rapid exploration of microprocessor designs. FPGAs are made up of thousands of small interconnected lookup tables that can be used to iterate easily in an incremental design debug cycle similar to software development life cycle. Therefore, FPGA accelerated simulators are faster than the software-only simulators.

Researcher(s): Khyamling Parane and Prabhu Prasad

Fusion of Remote Sensing Satellite Images

Coming soon ...

Researcher(s): Rahul C

Apart from the research scholars, a bunch of Masters and B.Tech students are working on a few exciting problems involving architecture simulation, large graph processing and Deep learning in computer vision.

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