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Deep learning algorithm engineer – model compression

159505
Beijing Shi, China, China
Jan 12, 2021

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Job Description

Description

At Xilinx, we are leading the industry transformation to build an adaptable, intelligent world. ARE YOU bold, collaborative, and creative? We develop leaders and innovators who want to revolutionize the world of technology. We believe that by embracing diverse ideas, pushing boundaries, and working together as ONEXILINX, anything is possible.

Our culture of innovation began with the invention of the Field Programmable Gate Array (FPGA), and with the 2018 introduction of our Adaptive Compute Acceleration Platform (ACAP), has made a quantum leap in capability, solidifying our role as the adaptable platform supplier of choice. From the beginning, we have always believed in providing inventors with products and platforms that are infinitely adaptable. From self-driving cars, to world-record genome processing, to AI and big data, to the world's first 5G networks, we empower the world's builders and visionaries whose ideas solve every day problems and improve people's lives.

If you are PASSIONATE, ADAPTABLE, and INNOVATIVE, Xilinx is the right place for you! At Xilinx, we care deeply about creating significant development experiences while building a strong sense of belonging and connection. We champion an environment of empowered learning, wellness, community engagement, and recognition, so you can focus on work that matters - world class technology that improves the way we live and work. We are ONEXILINX.

Job Responsibilities:
-- Research and develop neural network compression algorithms, focusing on quantization and pruning;
-- Develop neural network compression tool chain, connecting software and hardware;
-- Stay up to date with the latest neural network compression algorithm trends and deep learning framework (Tensorflow,Pytorch) trends;

 

Job Requirements:
-- Master or above degrees in computer science or related majors;
-- Good knowledge of machine learning/deep learning;
-- Experience in at least one deep learning framework, such as Tensorflow, Pytorch;
-- Experience in academic papers is preferred, such as CVPR, ICCV, ECCV, NIPS, ICLR,TPAMI.

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