Dipl.-Ing. Thomas Goldbrunner

Wissenschaftlicher Mitarbeiter  

Technische Universität München
Fakultät für Elektrotechnik und Informationstechnik
Lehrstuhl für Integrierte Systeme
Arcisstr. 21
80290 München

Tel.: +49.89.289.23871
Fax: +49.89.289.28323
Gebäude: N1 (Theresienstr. 90)
Raum: N2137
Email: thomas.goldbrunner@tum.de

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Titel
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Architectures for Neuromorphic Computing

Architectures for Neuromorphic Computing

Beschreibung

The goal of neuromorphic computers is to mimic the behaviour of the human nervous system or brain. Since the behaviour of neurons differs greatly from how classical computer systems work there is a need for new architectures. The approaches range from specialized CMOS designs over MOSFET based architectures to memristor based approaches. The goal of this seminar is to present the challenges posed by neuromorphic computing and how different architectures approach them.

Betreuer:

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Architectures for Neuromorphic Computing

Architectures for Neuromorphic Computing

Beschreibung

The goal of neuromorphic computers is to mimic the behaviour of the human nervous system or brain. Since the behaviour of neurons differs greatly from how classical computer systems work there is a need for new architectures. The approaches range from specialized CMOS designs over MOSFET based architectures to memristor based approaches. The goal of this seminar is to present the challenges posed by neuromorphic computing and how different architectures approach them.

Betreuer:

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Energy Efficiency of Neural Networks

Energy Efficiency of Neural Networks

Beschreibung

Deep and Convolutional Neural Networks are currently the de-facto standard when
it comes to machine learning and in the past years there have been great advances regarding their performance. However, with the wide adoption of these
techniques in data-centers around the world, energy efficiency becomes a more
and more important aspect. Therefore, the goal of this seminar is to provide an
overview of neural network implementations in software and hardware with regard to their energy efficiency.

Betreuer:

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Energy Efficiency of Neural Networks

Energy Efficiency of Neural Networks

Beschreibung

Deep and Convolutional Neural Networks are currently the de-facto standard when
it comes to machine learning and in the past years there have been great advances regarding their performance. However, with the wide adoption of these
techniques in data-centers around the world, energy efficiency becomes a more
and more important aspect. Therefore, the goal of this seminar is to provide an
overview of neural network implementations in software and hardware with regard to their energy efficiency.

Betreuer:

Laufende Arbeiten

Publikationen

  • Thomas Goldbrunner, Thomas Wild, Andreas Herkersdorf: Memory Access Pattern Profiling for Streaming Applications Based on MATLAB Models. 28th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS), 2018 mehr… BibTeX Volltext ( DOI )