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Reconfigurable/Spatial computing architectures, such as FPGAs, CGRAs, and AI accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs
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Experience in power converter control, and digital control based on FPGAs Experience in the use of GaN and SiC switches for power conversion Experience in the use of electrical simulation tools and the
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++. Experience in RTL design and FPGA prototyping using VHDL/Verilog and/or HLS tools is highly desirable A research-oriented attitude along with a result-focused mindset. Ability to work in a collaborative
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. Nice to have: Practical experience with machine-learning frameworks (e.g., PyTorch). Prior tape-out experience (ASIC or a complex FPGA prototype) and familiarity with the digital back-end flow (synthesis
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, Computer Science, or related field with excellent grades. Sound knowledge of computer hardware design and synthesis tools (ASIC, FPGA). Good programming and scripting skills. Excellent English communication
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synthesis tools (ASIC, FPGA). Good programming and scripting skills. Excellent English communication, presentation, and writing skills. Must be a team player. Knowledge of computing-in-memory is an added
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design and synthesis tools (ASIC, FPGA). Good programming and scripting skills. Excellent English communication, presentation, and writing skills. Must be a team player. Knowledge of computing-in-memory is
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provide a performance or efficiency advantage, and determine scenarios where conventional AI accelerators (such as embedded GPUs or FPGA-based accelerators) remain more appropriate due to data
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of DC/DC converter design and verification Experience of power converter control Experience of digital control and FPGA programming Experience in the use of GaN and SiC switches for power conversion