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Master Thesis in Extending GEMM for Time-series DSP Algorithms

Bosch Group

Renningen

Vor Ort

EUR 40.000 - 60.000

Vollzeit

Vor 6 Tagen
Sei unter den ersten Bewerbenden

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Zusammenfassung

Bosch Group invites candidates for a 6-month thesis internship centered on algorithm development for CNNs in processing acoustic signals. Applicants should have a Master's study background in Electrical Engineering or Computer Science, with proficiency in Python and Digital Design. Enthusiasm for technology innovations is crucial.

Qualifikationen

  • Background in Digital Design and Neural Networks.
  • Fluent in English, with German as a plus.
  • Strong organizational skills and independence.

Aufgaben

  • Develop and optimize algorithms for acoustic signals using CNNs.
  • Investigate input data representations and DSP techniques.
  • Focus on hardware design considerations for neural networks.

Kenntnisse

Digital Design
Python
Neural Networks

Ausbildung

Master studies in Electrical Engineering, Computer Science

Jobbeschreibung

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 GmbH is looking forward to your application!

Job Description

Prior to feeding data to neural networks, spectrum is typically generated using sliding window FFT and MFCC on acoustic signals. This approach treats acoustic signals as images, and image-based neural networks such as CNNs are utilized to perform tasks such as keyword spotting and denoising.

  • Extracting temporal and frequency information from the spectrum requires heavy pre-processing. In combination with CNN-based neural networks, you will develop and optimize algorithms to leverage the gains provided by GEMM-based accelerators.
  • You will explore different approaches to exploit features present in acoustic signals. Leveraging time encoding neural networks, the time series characteristics of acoustic signals can be better represented with lightweight pre-processing.
  • Furthermore, you will investigate various input data representations and DSP-heavy pre- and post-processing techniques to analyze acoustic scenes, enabling efficient mapping on GEMM-based accelerators.
  • Finally, hardware design considerations will be central to designing and optimizing processing chains, including neural network design, to make hardware implementation feasible.
Qualifications
  • Education: Master studies in Electrical Engineering, Computer Science, or a comparable field.
  • Experience and Knowledge: in Digital Design, (System)Verilog/VHDL, Python; background in Neural Networks.
  • Personality and Working Practice: you excel at organizing tasks in a structured manner and working independently.
  • Enthusiasm: keen interest in future technologies and trends with a passion for innovation.
  • Languages: fluent in English; German is a plus.
Additional Information

Start: according to prior agreement
Duration: 6 months

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

Diversity and inclusion are integral to our culture. We welcome all applications regardless of gender, age, disability, religion, ethnicity, or sexual orientation.

Need further information about the job?
Andre Guntoro (Functional Department)
+49 152 588 13129

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