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will be involved in crafting and applying high-accuracy algorithms for a Spiking Neural Network (SNN) processing unit, to be executed on FPGA and ASIC. As a Postdoc, your key responsibilities will be
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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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. 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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Experience with VLSI design (Cadence tools, Verilog/VHDL, SPICE) Knowledge of neural networks and neuromorphic systems is a strong advantage Good programming skills (e.g., Python, MATLAB) and interest in
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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