07. – 10. April 2019, Dagstuhl-Seminar 19152

Emerging Hardware Techniques and EDA Methodologies for Neuromorphic Computing


Krishnendu Chakrabarty (Duke University – Durham, US)
Tsung-Yi Ho (National Tsing Hua University – Hsinchu, TW)
Hai Li (Duke University – Durham, US)
Ulf Schlichtmann (TU München, DE)

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Gemeinsame Dokumente


Arguably the most exciting advancement in Artificial Intelligence in the past decade is the wide application of deep learning systems. Cultivated from conventional neural network and machine learning algorithms, deep learning introduces multiple layers with complex structures or multiple nonlinear transformations to model a high-level abstraction of the data. The ability to learn tasks from examples makes deep learning particularly attractive to cognitive applications such as image and speech recognition, object detection, natural language processing, etc. Nonetheless, the rapidly growing speed of the learning model size in state-of-the-art applications far exceeds the improvements of microprocessor computing capacity and computer cluster size. In addition, it has been widely accepted that conventional computing paradigms will not be scalable for these machine intelligence applications due to quickly increased energy consumption and hardware cost. This worry motivated active research on new or alternative computing architectures.

Neuromorphic computing systems, that refer to the computing architecture inspired by the working mechanism of human brains, have gained considerable attention. The human neocortex system naturally possesses a massively parallel architecture with closely coupled memory and computing as well as unique analog domain operations. The simple unified building blocks (i.e., neurons) follow integrate-and-fire mechanisms, leading to an ultra-high computing performance beyond 100 TFLOPs (Trillion FLoating-point Operations Per Second) and a power consumption of mere 20 Watt. By imitating such structure, neuromorphic computing systems are anticipated to be superior to conventional computer systems across various application areas. In the past few years, a number of large-scale neuromorphic projects have emerged, taking the approach to unprecedented scales and capabilities. Examples include IBM’s TrueNorth chip, Intel’s Loihi chip, the SpiNNaker machine of the EU Human Brain Project, the BrainScaleS neuromorphic system developed at the University of Heidelberg, etc.

To enable large-scale neuromorphic computing systems with real-time learning capability, design methodologies for effective and efficient functionality verification, robustness evaluation and chip testing/debugging become essential and important. Hardware innovation and electronic design automation (EDA) tools are required to realize energy-efficient and reliable hardware for machine intelligence on cloud servers for extremely high performance as well as edge devices with severe power and area constraints.

The goal of the Dagstuhl Seminar is to bring together experts in order to present and to develop new ideas and concepts for design methodologies and EDA tools for neuromorphic computing systems. Topics to be discussed include but are not limited to the following:

  • Design methodology for neuromorphic computing systems
  • EDA methodologies and tools
  • Architecture designs such as custom temporal parallel approach and dataflow designs
  • Implementations of synaptic plasticity, short-term adaptation and homeostatic mechanisms
  • Neuromorphic system integration and demonstration
  • Fault tolerance and reliability
  • Testing and debugging for neuromorphic systems

As possible results, we expect to see a better understanding of the respective areas, new impulses for further research directions, and ideas for areas that will heavily influence research in the domain of hardware and design automation for neuromorphic computing systems within the next years. The seminar will facilitate greater interdisciplinary interactions between researchers in neuroscience, chip designers, system architects, device engineers, and computer scientists.

Motivation text license
  Creative Commons BY 3.0 DE
  Krishnendu Chakrabarty, Tsung-Yi Ho, Hai Li, and Ulf Schlichtmann


  • Artificial Intelligence / Robotics
  • Hardware
  • Modelling / Simulation


  • Neuromorphic computing
  • Nanotechnology
  • Hardware design
  • Electronic design automation
  • Reliability and robustness


In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.


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Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.

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