Advanced Development/Research Munich 24.04.2024

Master Thesis HW-SW-Co-Design - Efficient Convolutions for DNN Compression (f/m/x)

SOME IT WORKS. SOME CHANGES WHAT'S POSSIBLE.

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More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly novel solutions. And 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. 

Description

We, the BMW Group, offer you an interesting and varied Master thesis in the area of efficient numerical design for convolutional neural networks and deployment paths on diverse target hardware.

In recent years, Convolutional Neural Networks (CNNs) have been the preferred architecture for vision-based tasks such as classification, semantic segmentation, and object detection. Deploying the networks to multiple devices suffers from a number of challenges, such as numerics (quantization), computational efficiency (no of operations, latency, realtime capability), and contention with other workloads. To overcome thes challenges, innovative means of neural network compression and neural architecture search need to be combined in cross-layer systems optimization.

 

What awaits you?

  • Research state-of-the-art CNN compression tecchniques.
  • Gain practical AI experience by implementing a novel method of quantization and pruning.
  • Utilize our cutting-edge training infrastructure to conduct experiments and evaluate your approach efficiently.
  • Present the thesis results using the scientific method, both in written and oral formats.
  • Collaborate with an experienced team that has published at international peer-reviewed conferences.
  • Engage with an international and diverse team at the Autonomous Driving Campus in Unterschleißheim.
     

Please note that your thesis must be supervised by a university on your part.

 

What should you bring along?

  • You are a master student approaching the end of your degree in computer science or related fields with focus on machine learning or artificial intelligence.
  • You have strong knowledge in computer vision concepts, tasks and and ideally comptuational design for efficient algorithms and numerics.
  • You have excellent programming skills in Python, TensorFlow and worked with modern programming environment tools such as Docker and Git.
  • You speak English fluently.
  • You are driven by curiosity and motivated to solve problems independently as well as sharing ideas and working in a team.

 

What do we offer?

  • Comprehensive mentoring & onboarding.
  • Personal & professional development.
  • Flexible working hours.
  • Digital offers & mobile working.
  • Attractive remuneration.
  • Apartment offers for students (subject to availability & only Munich).
  • And many other benefits - see bmw.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: from 05/20/2024

Duration: 6 months

Working hours: Full-time


Contact:
BMW Group HR Team
+49 89 382-17001

 

TRAVEL THROUGH THE WORLD OF MOBILITY: EXPERIENCE WHAT MOVES US.
Would you like to get to know the BMW Group as an employer and find out how you can use your talent with us? 
At our Students Day on 11.05.2024 in Munich, we offer you the opportunity to do so! You can find all information at bmwgroup.jobs/studentsday

Master Thesis HW-SW-Co-Design - Efficient Convolutions for DNN Compression (f/m/x)
20240424
Automotive
Munich
Germany
Legal Entity:
BMW AG
BMW Group
Location:
Munich
Job Field:
Advanced Development/Research
Job Id:
128121
Publication Date:
24.04.2024
FullTime
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