Master Thesis in Exploring Deployment Optimizations towards HW/SW Codesign - remote possible

Robert Bosch GmbH; Renningen; Remote; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

Robert Bosch GmbH is offering a Master's thesis opportunity focused on exploring deployment optimizations for neural networks. The successful candidate will join an international team, conduct research, and implement solutions for AI hardware acceleration while being enrolled at a university.

Qualifikationen

• Master studies in Electrical Engineering, Computer Science or comparable required.
• Strong experience with Python/C++ and neural network compiler (TVM).
• Highly motivated and excel at independent work.

Aufgaben

• Conduct literature research on NN deployment optimization strategies.
• Implement solutions in torch framework and evaluate in simulations.
• Document and discuss results.

Kenntnisse

Python

C++

Deep Learning Concepts

Linux

Ausbildung

Master studies in Electrical Engineering or Computer Science

Tools

neural network compiler (TVM)

LaTeX

Jobbeschreibung

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Master Thesis in Exploring Deployment Optimizations towards HW/SW Codesign - remote possible, Renningen

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Job Reference:

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Posted:

28.06.2025

Expiry Date:

12.08.2025

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Job Description:

Master Thesis in Exploring Deployment Optimizations towards HW/SW Codesign - remote possible

Full-time

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbHis looking forward to your application!

Job Description

Neural networks (NNs) are commonly used in wide variety of applications. Since NNs exhibit enormous computational demands, deploying these neural networks on the edge or embedded platform offers various optimization opportunities. To embrace hardware diversity, it is important to map the neural network on HW efficiently. It involves exploring graph-level intermediate representation in various neural network compilers such as TVM, IREE etc. towards HW/SW codesign framework.

• During your thesis you will join our international team of researchers and engineers and contribute to the exploration of hardware-based optimization for AI acceleration.
• You will conduct literature research on state-of-the-art NN deployment optimization strategies. You will evaluate related work in terms of its suitability for an overarching interaction between efficient HW/SW codesign.
• Furthermore, you will investigate, evaluate, and optimize NN deployment methods and compiler tool stack particularly focusing on graph optimizations and strategies such as e.g. operation fusion or reordering.
• You will implement your solution in our torch framework and evaluate it in extensive simulations.
• Finally, you will document and discuss the results.

Qualifications

• Education: Master studies in the field of Electrical Engineering, Computer Science or comparable
• Experience and Knowledge: strong experience in Python/C++, neural network compiler (TVM), basic hardware knowledge, Deep Learning Concepts, Linux, LaTeX
• Personality and Working Practice: you excel at working independently, organizing tasks effectively, and staying highly motivated to achieve your goals
• Work Routine: mobile working within Germany
• Languages: good in English

Additional Information

Start: according to prior agreement
Duration: 6 months

You want to work flexibly from your home in Germany or prefer working at the Bosch location in Renningen? For positions with the addition “remote possible”, you can agree on the appropriate collaboration for your task together with your manager and your team within the framework of Smart Work.

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?
Shubham Rai (Functional Department)
[phone]