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focused on the challenge of accelerating ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track
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ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track record in conducting machine learning research
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sensing hardware platforms. Demonstrated experience in supervising the digital implementation of advanced DSP blocks on FPGA and ASIC platforms. Strong background in supporting the design and modelling
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for manufacture. Knowledge of FPGA development and hardware description languages such as SystemVerilog or VHDL, with experience in simulation, synthesis, and implementation considered highly desirable
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of background checks, including: National police history Basic credit check Bankruptcy and personal insolvency ASIC banned/disqualified persons Anti-money laundering and counter-terrorism financing screening
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software, including microwave/microelectronics systems and low-level programming languages such as C/C++ and/or FPGA hardware design languages. Strong organisational and time management skills, with
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, augmented virtual reality, voice assistance, and medical diagnosis. This new paradigm is further accelerated by AI chips and ASICs embedded on mobile devices, e.g., Apple’s Bionic neural engine. Compared