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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 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