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Nikhil Gaikwad


Nikhil Gaikwad

During my PhD studies, I worked on the hardware acceleration of Machine Learning (ML) algorithms for real-time and power-efficient sensor data analytics at the IoT edge. My current working experience includes FPGA acceleration of Artificial Neural Network (ANN), smart sensor network, smart WSN, embedded system design, rapid prototyping and edge intelligence for IoT. I have implemented various novel ANN-based architectures to analyse heterogeneous sensor data at the edge gateway of intelligent wearables. Before joining the doctoral research, I worked as a research scientist for one year. I have contributed to the embedded system development that includes algorithm designing, hardware implementations, debugging, interfacing, field installation, and testing during this role. I was extensively involved in developing the hardware system porotypes that used FPGA, SoCs, and processor systems. I am currently working on Surface Nuclear Magnetic Resonance (NMR) project in collaboration with the Hydrogeophysics Group at Aarhus University. My research interests include; embedded and digital system devolvement, FPGA based edge intelligence, reconfigurable systems, NMR systems, machine learning and the Internet of Things.

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