Code : 5LIL0
Credits: 5 ECTS Lecturer : Prof. dr. Henk Corporaal + several Guest lecturers
(see the actual schedule) Tel. : +31-40-247 5195 (secr.) 5462 (office) Email: H.Corporaal at tue.nl Project assistance: Berk Ulker (b.ulker at tue.nl), Kanishkan
Vadivel (k.vadivel at tue.nl),
Martin Roa Villescas (m.roa.villescas at tue.nl),
Sherif Eissa (s.s.b.eissa at tue.nl), Ali Banagozar (a.banagozar at
Floran de Putter (f.a.m.d.putter at tue.nl),
Mohammad Emad (m.emad at tue.nl) Material: check canvas and
below. Previous year: check here
Mar 29: All topic slides are now online. Recap slides will
learning and in particular deep learning has dramatically
improved the state-of-the-art in object detection, speech
recognition, robotics, and many other domains. Whether it is
superhuman performance in object recognition or beating human
players in Go, the astonishing success of deep learning is
achieved by deep neural networks trained with huge amounts of training
examples and massive
computing resources. Although already applied successfully
in academic use-cases and several consumer products (e.g.
machine translation), these data and computing requirements pose
challenges for further market penetration.
course on Intelligent Architectures first treats the most
important Deep Learning Networks. In particular we treat how
they operate, their implementation, and how they perform
learning. We will use standard frameworks, like Tensorflow or
PyTorch, for building these networks.
networks require lots of computation and memory accesses,
making them costly and very energy consuming. Therefore this
Intelligent Architectures course gives an in-depth treatment
of several Network and Implementation optimizations steps,
like network pruning, quantization and loop-nest
transformations for a drastic reducing of computation and
memory traffic requirements.
treat various processing and accelerator platforms tuned for
deep learning algorithms, including (embedded) GPU, Tensor
Processing Unit, and TTA (Transport Triggered Architectures)
tuned for Deep ANNs (Artificial Neural Networks). Tuning the
architecture and/or adding specific hardware can lead to huge
we will look in the future, and hint on what other high
potential machine learning approaches can offer, like Bayesian
learning and Neuromorphic computing.
course includes 2-3 lab assignments, covering above topics.
The labs give you real hand-on experience on designing and
You will learn:
-understanding deep learning, including
network architectures, inference, and learning methods.
-how to design Deep Artificial Neural
-how to implement and optimize ANNs
using various optimization methods.
-state-of-the-art ANNs, including the
newest type of operators.
-special processign architectures and
hardware efficiently supporting Deep Learning.
-alternative approaches to the
''classical'' ANNs, like Bayesian learning and Neuromorphic
The main emphasis is on Deep Learning, in particular on ANNs
(Artificial Neural Networks), its algorithms, and its Efficient
Implementation, using custom and off-the-shelve processors and
accelerators. Note often we talk about DNNs (Deep Neural Networks);
usually we refer then to Deep ANNs, and not Deep SNNs (Spiking
In this course we treat among others the following topics:
CNN: Convolutional Neural Networks
Frameworks for designing DNNs (Deep Neural Networks)
Quantization of activations and weights in DNNs
Advanced mapping of DNNs exploiting data reuse for
activations and weights
General architecture support for DNNs
Beyond the classic neural networks
Most of the topics will be supplemented by very elaborate hands-on
For a preliminary lecture overview see: schedule.
As part of this lecture you have to study a hot topic related to
this course, and make a short slide presentation about this topic. Details
will be announced during the lecture.
Guidelines are as follows:
Choose one hot topic
which interests you and which is highly related to this
Select one technical (in depth) research paper from the web,
based on this topic. See lists below.
The paper should be published in 2018 or later.
The paper should have
sufficient technical depth; i.e. it should clearly
explain all the details of the proposed method or solution. So
e.g. do not choose company white or business papers. You can
also check whether the paper is from well perceived journals or
conferences, like IEEE, or ACM conferences and journals
(see e.g. IEEE.org, and ACM.org).
Check the top
conferences and top
journals on Machine Learning and Artificial
You may also have a look at the following two lists:
Top architecture conferences, containing lots of Deep
Learning Architecture and Implementation papers:
ISCA: International Symposium on Computer
IEEE MICRO: Symposium on Microprocessor
Architectural support for languages and operating systems:
International Conference on Supercomputing:
International Solid State Circuits Conference: isscc.org
Design Automation Conference: www.dac.com
Design Automation and Test in Europe: www.date-conference.com
(Hardware-Software Codesign) + ISSS (International Symposium
on System Synthesis): www.codes-isss.org
Compilers, Architectures, and Synthesis for Embedded Systems:
MICRO: Symposium on Micro Arch: www.microarch.org
Top conferences on Machine Learning & Artificial
Intelligence, containing also Deep Learning Architecture
and Implementation papers:
NeurIPS: Neural Information
Processing Systems (NIPS)
ICML: International conference on
CVPR : IEEE/CVF Conference on Computer Vision and Pattern
IEEE/CVF International Conference on Computer Vision
European Conference on Computer Vision ·
AAAI : AAAI Conference on Artificial Intelligence
A larger list on computer architecture related conferences and
journals can be found here.
You should make a powerpoint presentation on your topic; max 5 min. per presentation
(e.g. 5 slides;
one slide introducing the problem, then the approach and results
of each paper, and final conclusion and suggestions from your
side on this topic; add / use clear pictures to explain the approach)
The presentation should contain at least the following:
the paper contributions (including technical details)
of the paper and topic
applicability of proposed methodology / solution
indicate new / future directions of research
In order to evaluate the paper you may wish to read related material on the same
Your presentation will be evaluated by us. This evaluation
will be taken into account for the final grading.
Hands-on lab work (will be
Becoming an expert in Deep Learning and Deep Neural Networks
requires that you get your hands dirty and make practical
assignments. Therefore, as part of this course we have put a lot of
effort to prepare 3 very interesting lab assignments. Details will
be presented during the course, and material will be placed on the
oncourse 5LIL0 site.
** labs will be put online during the course **
You will design a deep Convolutional Neural Network (CNN) using one
of the well-known frameworks: PyTorch. The network has to recognize
After learning the network will be tuned: pruning, quantization and
Hands-on 2: DNN
implementation on GPUs
Graphic processing units (GPUs) can contain upto thousands of
Processing Engines (PEs). They achieve performance levels of Tera
FLops (10^12 floating point operations per second). In the past GPUs
were very dedicated, not general programmable, and could only
be used to speedup graphics processing. Today, they become
more-and-more general purpose. Lately they also support Deep Neural
Networks (DNNs) by supporting smaller data sizes (e.g. Float 16-bit)
and having special units speeding up learning and inference.
In this lab you are asked to map a DNN efficiently on a GPU, using
all the tricks you can play.
3: DNN implementation on Embedded ASIP
In this lab we will map a Deep Neural Network (DNN) to an
Application Specific Instruction-set Processor (ASIP).
We will use the AivoTTA from Tampere University as a target
platform. You can tune the platform by adding specific function
See the lab3 assignment.
Further files an details are on oncourse.tue.nl
The examination will be oral about the treated course theory, the
lab report(s), and studied articles.
We will discuss the dates with you.
Grading depends on your results on theory, lab exercises and
defense, and your presentation.