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  • TU Braunschweig
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  • Institute of Neuromorphic Computing
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Logo Institut für Neuromorphic Computing
Neuromorphic Computing and Engineering
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    • Emerging Memory Technologies
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    • Neuromorphic Computing
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Neuromorphic Computing and Engineering

Neuromorphic Computing and Engineering (Neuromorphes Rechnen und Technologie, NCE) is the institute's core lecture. Neuromorphic computing is an approach inspired by the structure and function of the human brain. Neuromorphic computers are based on silicon devices that use physical artificial neurons and synapses to perform computations. Today the term describes analog, digital and mixed-mode VLSI systems as well as software systems that implement models of neural systems. At the hardware level, neuromorphic computing can be realised with oxide-based resistive or ferroelectric memristors, among others.

The lecture presents the principles of biological computation and their implementation in hardware. Basic building blocks of neuromorphic technology are covered and the implications for the development of novel information processing technologies are outlined. The exercises complement the lecture with programming tutorials and simulation tasks.

Abstrakte Darstellung eines spikenden neuronalen Netzes

At a glance

  • Type: Lecture (2420000012) with exercises (2420000013)
  • Cycle: every summer semester
  • Scope: 2 contact hours lecture and 1 contact hour exercises per week, 5 credit points (42 h contact time, 108 h self-study)
  • Language: English
  • Examination: oral exam, 30 minutes
  • Programmes: Master Electrical Engineering (specialisation Information Technology Systems), Master Industrial Engineering/Electrical Engineering, Master Computer Science, Master Microelectronics Engineering (elective)
  • Module coordinator and lecturer: Prof. Dr. Thomas Kämpfe
  • Registration and materials: Stud.IP

Contents

  • Biological foundations: neurons, synapses and principles of information processing in the brain
  • Neuron models and their implementation in analog, digital and mixed-signal CMOS circuits
  • Spiking neural networks, encoding of information in spikes and learning rules
  • Event-based sensing and communication in neuromorphic systems
  • Compute-in-memory and memristive technologies: resistive and ferroelectric devices as artificial synapses
  • Building blocks and architectures of neuromorphic hardware, implications for novel information processing technologies
  • Exercises: programming tutorials and simulation tasks on neuromorphic networks

Learning outcomes

After successfully completing the module, students understand the principles of biological computation. They know how to implement them in hardware and have basic skills in applying neuromorphic engineering.

Literature

  • Neuromorphic Computing for Computer Scientists
  • S.-C. Liu et al., Event-Based Neuromorphic Systems. Wiley, 2014, ISBN 9780470018491
  • Z. Sun et al., A full spectrum of computing-in-memory technologies. Nature Electronics 6, 823 (2023)
  • C. Schuman et al., Opportunities for neuromorphic computing algorithms and applications. Nature Computational Science 2, 10 (2022)

Dates and registration

Summer semester 2026: lecture Fridays 15:00 to 16:30 and exercises Fridays 13:15 to 14:45, lecture hall HS 66.3 (Hans-Sommer-Strasse 66). The next offering is planned for the summer semester 2027, registration is via Stud.IP.

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