April 7 – 10 , 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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Dagstuhl Report, Volume 9, Issue 4 Dagstuhl Report
Aims & Scope
List of Participants


The explosion of big data applications imposes severe challenges of data processing speed and scalability on traditional computer systems. However, the performance of von Neumann architecture is greatly hindered by the increasing performance gap between CPU and memory, motivating 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. By imitating this structure, neuromorphic computing systems are anticipated to be superior to conventional computer systems across various application areas. In the past few years, extensive research studies have been performed on developing large-scale neuromorphic systems. Examples include IBM's TrueNorth chip, the SpiNNaker machine of the EU Human Brain Project, the BrainScaleS neuromorphic system developed at the University of Heidelberg, Intel's Loihi etc. These attempts still fall short of our expectation on energy-efficient neuromorphic computing systems with online, real-time learning and inference capability. The bottlenecks of computation requirements, memory latency, and communication overhead continue to be showstoppers. Moreover, there is a lack of support in design automation of neuromorphic systems, including functionality verification, robustness evaluation and chip testing and debugging. Hardware innovation and electronic design automation (EDA) tools are required to enable energy-efficient and reliable hardware implementation 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 seminar was to bring together experts from different areas in order to present and to develop new ideas and concepts for emerging hardware techniques and EDA methodologies for neuromorphic computing. Topics that were discussed included:

  • Neuroscience basics
  • Physical fundamentals
  • New devices and device modeling
  • Circuit design and logic synthesis
  • Architectural innovations
  • Neurosynaptic processor and system integration
  • Design automation techniques
  • Simulation and emulation of neuromorphic systems
  • Reliability and robustness
  • Efficiency and scalability
  • Hardware/software co-design
  • Applications

The seminar facilitated greater interdisciplinary interactions between physicists, chip designers, architects, system engineers, and computer scientists. High-quality presentations and lively discussions were ensured by inviting carefully selected experts who participated in the seminar. All of them have established stellar reputations in the respective domains. As a result, we developed a better understanding of the respective areas, generated impetus for new research directions, and ideas for areas that will heavily influence research in the domain of neuromorphic design over the next years.

At the end of the seminar, we identified the following four areas as being among the most important topics for future research: computing-in-memory, brain-inspired design and architecture, new technologies and devices, and reliability and robustness. These research topics are certainly not restricted to and cannot be solved within one single domain. It is therefore imperative to foster interactions and collaborations across different areas.

Summary text license
  Creative Commons BY 3.0 Unported license
  Hai Li


  • Artificial Intelligence / Robotics
  • Hardware
  • Modelling / Simulation


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


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