227-0085-09L Projects & Seminars: Spiking Neural Network on Neuromorphic Processors
Semester | Spring Semester 2021 |
Lecturers | G. Indiveri |
Periodicity | every semester recurring course |
Course | Does not take place this semester. |
Language of instruction | English |
Comment | Only for Electrical Engineering and Information Technology BSc. The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. |
Courses
Number | Title | Hours | Lecturers | |
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227-0085-09 P | Projekte & Seminare: Spiking Neural Network on Neuromorphic Processors
![]() Does not take place this semester. Für den Zugang zum Angebot und zur Einschreibung loggen Sie sich hier ein (mit Ihrem n.ETHZ account): https://psapp.ee.ethz.ch/ Bitte beachten Sie, dass die Seite jeweils erst zwei Wochen vor Semesterbeginn zugänglich ist und im Verlauf des Semesters wieder abgeschaltet wird. Die Einschreibung ist nur von Freitag vor Semesterbeginn bis zum ersten Freitagmittag im Semester möglich. To access the offer and to enroll for courses log in (with your n.ethz account): https://psapp.ee.ethz.ch/ Please note that the P&S-site is accessible no earlier than two weeks before the start of the semester until four weeks after the start of the semester. Enrollment is only possible from Friday before the start of the semester until noon of the first Friday in the semester. | 3 hrs | G. Indiveri |
Catalogue data
Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. |
Learning objective | Machine Learning – Spiking Neural Network – DVS Cameras - Programming Neuromoripch processors – Intel Loihi - Final Project with a presentation. Compared to the “traditional” artificial neural network, the spiking neural network (SNN) can provided both latency and energy efficiency. Moreover, SNN has demonstrated in previous works a better performance in processing physiological information of small sample size, and only the output layer of the spiking neural network needs to be trained, which results in a fast training rate. This couse focuses on giving the bases of spiking neural networks and neuromorphic processors. Students will learn the tools to implement SNN algorithm in both academic processors and Intel Loihi using data from Event-based Vision camera and biomedical sensors (i.e. ECG and EEG). The course will end with 4 weeks project where the students can target a specif application scenario. The course will be taught in English. |
Performance assessment
Performance assessment information (valid until the course unit is held again) | |
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ECTS credits | 3 credits |
Examiners | G. Indiveri |
Type | ungraded semester performance |
Language of examination | English |
Repetition | Repetition only possible after re-enrolling for the course unit. |
Learning materials
No public learning materials available. | |
Only public learning materials are listed. |
Groups
No information on groups available. |
Restrictions
General | ![]() |
Places | Limited number of places. Special selection procedure. |
Beginning of registration period | Registration possible from 19.02.2021 |
Priority | Registration for the course unit is only possible for the primary target group |
Primary target group | Electrical Engin. + Information Technology BSc (228000) |
Waiting list | until 12.03.2021 |
End of registration period | Registration only possible until 05.03.2021 |
Offered in
Programme | Section | Type | |
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Electrical Engineering and Information Technology Bachelor | Projects & Seminars | W | ![]() |