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 and Schedule
Ca. 3 introductory lectures, and research paper presentations by participants.
Hybrid (zoom) multi-site PhD course format with guest lectures and remote participants from BTH and Lund University. Ca. 3 introductory lectures with mandatory attendance. Research paper presentation and opposition by each participant. Written or oral exam. All moments must be passed in order to pass the course.Ca. 3 introductory lectures, and research paper presentations by participants.
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
Selected survey articles about Neuromorphic Computing, and research papers for student presentations, to be announced later on this course web page.
Examination
Credit
3hp if both examination moments are fulfilled.
Both moments must be passed in order to obtain any credits for the course.
Comments
Introductory lectures probably in late August/September.
Student presentations as whole-day seminar in September or October 2026.
Exam on a half-day in October 2026.