Master Thesis FPGA Prototyping of Efficient DNN Accelerators (f/m/x)

Forschung / Vorentwicklung München 16.10.2023 Master Thesis FPGA Prototyping of Efficient DNN Accelerators (f/m/x) THE BEST INTERNSHIP IN THEORY - AND IN PRACTICE. SHARE YOUR PASSION. World-leading technologies don’t make it into a BMW until they’ve undergone one of the most challenging journeys imaginable. It takes dynamic teams with outstanding technical skills to take them from the drawing board to the road. That’s why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table – and give you the opportunity to really show what you can do. We, the BMW Group, offer you an interesting and challenging master thesis in the area of Hardware/Software Codesign. Deep Neural Networks (DNNs) have found their way into a wide range of computer vision tasks. Convolution layers have dominated the DNN architectures, and a variety of domain-specific accelerator architectures have been proposed to maximize their energy efficieny and performance. Recent research advocates the use of convolutional variants like dilated convolutions, grouped convolutions, depthwise convolutions and point-wise convolutions and it has been challenging to maximize the performance for these variants on resource-constrained embedded hardware. This thesis offers an exciting opportunity to investigate the impact of accelerator memory hierarchies and optimal dataflows for efficient mapping of DNN workloads on edge.   What awaits you? Literature survey of the state-of-the-art CNN accelerator designs. Experience in implementing advanced convolution algorithms and solving the challenges associated with quantization of operands. Engagement in a diverse team with experience in publishing at international peer-reviewed conferences. Scientific writing of your thesis and presentation of the research results both at the university and industry. Please note that your thesis must be supervised by a university on your part.   What should you bring along? Currently pursuing a master's degree in electrical engineering, computer science or a comparable qualification. Very good programming skills in Verilog/VHDL/HLS, C/C++,as well as substantial experience with Xilinx FPGAs. Practical knowledge of deep learning accelerators and their constraints on edge . Highly motivated and eager to collaborate in a team. Business-fluent English, both written and verbal.   What do we offer? Comprehensive mentoring & onboarding. Personal & professional development. Work-Life-Balance & flexible working hours. Digital offers & mobile working. Attractive remuneration. Apartment offers for employees (only Munich). And many other benefits - see jobs/benefits   You are enthused by new technologies and an innovative environment? Apply now!   At the BMW Group, we see diversity and inclusion in all its dimensions as a strength for our teams. Equal opportunities are a particular concern for us, and the equal treatment of applicants and employees is a fundamental principle of our corporate policy. That is why our recruiting decisions are also based on personality, experience and skills. Find out more about diversity at the BMW Group at bmwgroup.jobs/diversity Earliest starting date: 01.12.2023 Duration: 6 months Working hours: Full-time Contact:BMW Group HR Team+49 89 382-17001 Master Thesis FPGA Prototyping of Efficient DNN Accelerators (f/m/x) Unternehmensbereich: BMW AG Standort: München Arbeitsbereich: Forschung / Vorentwicklung Job ID: 108905 Veröffentlichungsdatum: 16.10.2023 JETZT BEWERBEN