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. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power electronics, and self
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for seizure detection. These algorithms will be implemented on a spiking neural network (SNN) processing unit deployed on FPGA and custom-designed chips with an integrated detection mechanism. Research area and
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energy-efficient CMOS blocks implementing SSM-based LLMs. Prototype hardware blocks on FPGA and prepare for ASIC tape-out. Benchmark performance and comparison with transformer accelerators. Work with
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activities, which span a diverse range of advanced electronic systems. These include FPGA and neuromorphic computing, edge AI, machine learning, sensing technologies, and energy harvesting—key components