PhD course:

Neuromorphic Computing (4hp)

HT/2026

Recommended for
PhD students with an interest in neural networks, advanced computing hardware and software techniques, energy-efficient computing, and parallel and distributed computing. The course is mandatory for new PhD students in the ELLIIT F project "ScEENeC" starting in 2026, whose co-PIs jointly organize the course.

Organization
Hybrid (zoom) multi-site PhD course format with guest lectures and remote participants from BTH and Lund University.

• Introductory lectures 3-4 September 2026 (half/whole days) with mandatory attendance.
  LiU participants will meet in room Donald Knuth, IDA (B, entry 29, upper floor).
• Research paper presentation and opposition by each participant, 7 October 2026 (whole day).
• Oral or written exam (dep. on number of participants), 30 October 2026 (whole day).

Lecture Schedule
The following information is preliminary and may be revised before course start.

• 09:00-10:00 Course overview; Lecture 1 - Fundamentals of biological neural networks and neuromorphic computing (Christoph Kessler, LiU)
• 10:00-11:00 Break (ScEENeC members will have a project meeting during this time on a different zoom channel)
• 11:00-12:15 Lecture 1 (cont.)
• 13:15-14:00 Guest lecture (Mattias Borg, LTH).
  LTH participants can attend physically in room E:1426.
• 14:00-15:00 Lecture 2 - Learning for SNNs I: ANN-SNN conversion, unsupervised learning (Flavius Gruian, LTH)
  LTH participants can attend physically in room E:1426.
• 15:00-15:30 Break (PELAB members will have a lab meeting during this time in a different room)
• 15:30-17:00 Lecture 3 - Learning for SNNs II: supervised learning (Christoph Kessler, LiU)

• 09:00-10:00 Lecture 4 - Neuromorphic Hardware (Håkan Grahn, BTH)
• 10:00-10:15 Break
• 10:15-11:15 Lecture 4 (cont.) - Reconfigurable Neuromorphic Systems; Examples of Neuromorphic Systems (Håkan Grahn, BTH)
• Short break
• 11:30-12:15 Distribution of papers for student presentation/opposition (all organizers)
• (still open if there might be a session in the early afternoon, probably not, TBD)

• 08:00-17:00 Student presentations
  For LiU students: room Donald Knuth.
  For LTH students: room E:4130.
  For BTH students: TBA
  Detailed schedule and zoom link will be available in the internal course repository.

Course material and zoom link

• Public material will be accessible here on the course web page.
• There is a separate repository for internal material such as zoom links; access information will be sent to registered course participants before course start.
• The list of proposed papers for student presentations is in Section 3 of the document in the internal repository.

The course was last given
This is a new course.

Goals/Contents
Energy consumption is one of the main challenges of today's AI systems. Most AI systems today are designed using various forms of neural networks and deep learning. However, training and even inference on conventional digital hardware, even with GPUs or NPUs, are very costly in terms of time, computational demands, and energy consumption. One promising alternative is spiking neural networks (SNNs) executed on neuromorphic hardware.
Neuromorphic computing tries to mimic how the brain works by relying on changes in signals, i.e., "spikes", rather than continuously recalculating numerical values as in traditional artificial neural networks, offering a promising and more energy-efficient alternative to traditional neural network-based systems for machine learning. First hardware realizations of neuromorphic hardware are available (e.g. Intel's Loihi2 or SpiNNcloud's SpiNNaker2), as well as software support. However, some technical challenges remain before neuromorphic computing can widely replace ANNs and conventional digital hardware for machine learning workloads.
This course will give a general introduction to fundamental concepts of neuromorphic computing, including SNNs, processing in memory (PIM), neuromorphic hardware and software, and application domains.
We will discuss the fundamentals, existing hardware and software techniques, opportunities and remaining challenges, and read selected recent research papers in neuromorphic computing.

Literature

A. Abdullah, K. Dang: Neuromorphic Computing Principles and Organization, 2nd edition. Springer, 2025.

Also, selected survey articles about Neuromorphic Computing:

• A. Gebregiorgis et al.: "Spike-based neuromorphic computing: An overview from bio-inspiration to hardware architectures and learning mechanisms." Microprocessors and Microsystems, Dec. 2025.
• D. Kudithipudi et al.: "Neuromorphic Computing at Scale", Nature 647:801-812, Jan. 2025.

• Active participation in the introductory lecture and presentation sessions
• Successful individual paper presentation with opposition
• Written or oral exam (depending on number of participants)

Credit

4hp if all examination moments are fulfilled.
All moments must be passed in order to obtain any credits for the course.

Comments
Introductory lectures 3-4 September.
Student presentations as whole-day seminar 7 October 2026.
Exam on a half-day, 30 October 2026.