Advanced School for Computing and Imaging (ASCI)

ASCI office
Delft University of Technology
Building 28, room 04.E120
Van Mourik Broekmanweg 6
2628 XE – DELFT, The Netherlands

P: +31 15 27 88032

Visiting hours office
Monday, Tuesday, Thursday: 10:00 – 15:00


The ASCI office is located at the Delft University of Technology campus.  It is easily accessible by bicycle, public transport and car. The numbers of buildings can help you find your way around the campus. Make sure you remember the name and building number of your destination.

Contact us at +31 15 278 8032 or send us an email at

A16 winter school on Efficient Deep Learning

A16 - ASCI Winterschool on Efficient Deep Learning

Date Nov 28 – Dec 1, 2023
Registration  Maximum number of participation reached


Slides Tuesday Nov 28, 2023

Slides Wednesday Nov 29, 2023

Slides Thursday Nov 30, 2023

Slides Friday Dec 1, 2023

Course content

Machine learning has numerous important applications in intelligent systems within many areas, like automotive, avionics, robotics, healthcare, well-being, and security. The recent progress in Machine Learning, and particularly in Deep Learning (DL), has dramatically improved the state-of-the-art in object detection, classification and recognition, and in many other domains. Whether it is superhuman performance in object recognition or beating human players in Go, the astonishing success of DL is achieved by deep neural networks. However, the complexity of DL networks for many practical applications can be huge, and their processing may demand a high computing effort and excessive energy consumption. Their training requires huge data sets, making the training even orders of magnitude more intensive than their already very demanding inference phase. A new development is to move intelligence from the cloud to the IoT edge; this further stresses the need to tame the complexity of DL and Deep Neural Networks.

This Winter School treats various topics addressing the complexity reduction of DL, including:

  • Architectural and Hardware accelerator support for DL, with emphasis on energy reduction, computation efficiency and/or computation flexibility, both for inference and/or for learning;
  • Spiking and brain-inspired neural networks and their implementation;
  • Efficient mapping of DL applications to target architectures, including many-core, GPGPU, SIMD, FPGA, and HW accelerators;
  • Exploiting temporal and spatial data reuse, sparsity, quantization and approximate computing, dynamic neural networks, and other methods, to decrease the complexity and energy demands of DL.
  • Efficient learning approaches, including data reduction, online learning, and quality of learning;
  • Tools, Frameworks and High-level programming language support for DL;
  • NAS: Neural Architecture Search, including Hardware aware NAS;
  • Advanced applications exploiting DL.

Above topics will be treated by experts from the Netherlands and abroad.

Required background: Basic knowledge of deep learning and computer architecture.

ASCI students can get 5 ECTS credits for this course. To get these credits they have to complete a lab/research study related to one or more of the treated topics.

Responsible Lecturer

Andy Pimentel (UvA)
Henk Corporaal (TUE)
Lydia Chen (TUD)

Education Period:

Nov 28-Dec 1, 2023

Time schedule:

Will follow soon.