Admission requirements
Assumed Prior Knowledge
A basic understanding of machine learning and deep learning (Machine Learning).
Basic programming skills of Python (Introduction to Programming).
Computer Architecture and Embedded system knowledge are highly prefered.
Description
How can we make AI as efficient as the human brain?
This course explores how to achieve energy-efficient AI by drawing inspiration from the brain, one of the most powerful and low-power intelligent systems known. As deep learning models like large language models (LLMs) continue to grow in size and complexity, running them efficiently on edge devices (such as smartwatches and VR headsets) and at scale is becoming an urgent challenge.
In this course, students will learn and explore a range of cutting-edge techniques for efficient AI, including model compression like pruning, quantization, and neuromorphic computing. We also explore the hardware frontier, examining how modern AI processors are optimized to support low-power, high-performance machine learning.
Course objectives
By the end of the course, students will be able to:
Explain key challenges and motivations behind energy-efficient AI computing.
Describe the principles of brain-inspired computing.
Apply model compression techniques such as pruning, quantization, and knowledge distillation to optimize deep learning models.
Evaluate trade-offs between model accuracy, latency, power consumption, and memory usage.
Understand the architecture and design of modern AI processors.
Design and conduct a project to explore energy-efficient or brain-inspired AI techniques in real-world applications.
Timetable
The most recent timetable can be found at the
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Weekly lectures
Practical sessions
Assessment method
This is a MSc level course, and by the end of this class you should have a good understanding of efficient deep learning techniques, and be able to deploy them on your laptop.
The final grade for this course is composed of:
Written exam (30%)
Lab assignments (30%)
One final project (40%)
Note:
Labs must be done individually, however, it is acceptable to collaborate when figuring out answers and to help each other solve the problems. The final project will be carried out in groups of up tp 4 people, and has two main parts:
oral presentation
final report (3 pages, using the NeurIPS template)
The grade for the written exam should be 5.5 or higher in order to complete the course. The average grade for the practical sessions should be 5.5 or higher in order to complete the course. If one of the tasks is not submitted the grade for that task is 0. Each assignment has a re-sit opportunity (a later submission). The maximum grade for a re-sit assignment is 6.
Reading list
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Dr. Qinyu Chen
Email: q.chen@liacs.leidenuniv.nl
[Education coordinator LIACS master] (mailto:mastercs@liacs.leidenuniv.nl)
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.