Manu Manuel, M.Sc.

Wissenschaftlicher Mitarbeiter

Technische Universität München
Fakultät für Elektrotechnik und Informationstechnik
Lehrstuhl für Integrierte Systeme
Arcisstr. 21
80333 München

Tel.: +49.89.289.28338
Fax: +49.89.289.28323
Gebäude: N1 (Theresienstr. 90)
Raum: N2116
Email: manu.manuel@tum.de

Curriculum Vitae

Manu Manuel has started his Ph.D. work in the topic "Approximate computing for professional image processing" in February 2019 at the Chair of Integrated Systems, Technical University of Munich. He received his Master of Science (M.Sc.) in Embedded Systems from the Chemnitz University of Technology in 2018. Manu has been involved in different projects at Hilti Deutschland AG, Kaufering and Robert Bosch GmbH, Hildesheim for eight months each as internships and thesis, and he completed his master thesis on the topic "Invalid Status Identification of Traffic Signs". Moreover, his master research project was the development of a novel computer vision algorithm for breath rate detection using the RGB data. In 2012, he completed his bachelor of technology (B. Tech) in Electronics and Communication Engineering from Mahatma Gandhi University (Amal Jyothi College of Engineering), India.

FORSCHUNG

  • Approximate computing on FPGA for image processing
  • Dynamic reconfiguration of FPGA
  • Image processing, computer vision and machine learning

Angebotene Arbeiten

BAMAIDPFPIPHSSHK
Titel
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Application of Machine Learning Based Approaches in FPGA design Optimization

Application of Machine Learning Based Approaches in FPGA design Optimization

Beschreibung

The ever-higher demands for computational capabilities of FPGA devices in the application domains like data analysis or image processing are forcing the researchers to rethink their conventional approaches to the system design. One alternative is approximate computing which performs inexact calculations instead of the actual one and brings out better performance, space and energy efficiency on hardware systems. However, it's essential to keep the application quality degradation due to such approximations below a tolerable limit. The machine learning-based approaches such as learning classifier systems or genetic algorithms play an important role in the identification of optimal FPGA design parameters which maximizes the above benefits with or without the approximations in their calculations.

This seminar aims to identify and analyze the applications of machine learning based approaches in the FPGA design optimization with or without approximations.

Kontakt

Manu Manuel, Room: N2116, manu.manuel@tum.de, +49 89 289 28338

Betreuer:

Manu Manuel
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Approximate computing Methods for FPGA-Based Image Processing

Approximate computing Methods for FPGA-Based Image Processing

Beschreibung

Digital image processing in professional applications places ever-higher demands so that the computing power and power consumption of FPGA devices reach their limits. Approximate computing provides a new design paradigm by performing inexact calculations instead of the actual one. As a result, fewer resources are used in the FPGA devices, more functions can be implemented, and the energy efficiency of the calculations is improved. However, approximate computing always trades off the application quality against these benefits. Hence. it's important to keep the quality degradation below a tolerable limit.

This seminar aims to identify the current trends in approximate computing on FPGA for image processing and analyzing the interesting approaches in detail.

Kontakt

Manu Manuel, Room: N2116, manu.manuel@tum.de, +49 89 289 28338

Betreuer:

Manu Manuel

PUBLIKATIONEN

  • Wiede, Christian; Richter, Julia; Manuel, Manu; Hirtz, Gangolf: Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component Analysis. Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISAPP, (VISIGRAPP 2017), SCITEPRESS - Science and Technology Publications, 2017, 326-333 mehr… BibTeX Volltext ( DOI )