Course syllabus

Minnesteknologi för maskininlärning
Memory Technology for Machine Learning

EITP25, 7,5 credits, A (Second Cycle)

Valid for: 2023/24
Faculty: Faculty of Engineering, LTH
Decided by: PLED C/D
Date of Decision: 2023-04-18

General Information

Main field: Nanoscience.
Elective for: E4-is, F4, F4-fel, MNAV1, MSOC2, N4-hn
Language of instruction: The course will be given in English


The purpose of this course is to give an in depth understanding for the physics of common memory device technologies with focus on non-volatile memories. Furthermore, the course covers how these memory devices can be integrated to create neuromorphic hardware for applications in machine learning and artificial intelligence. Finally, the course gives an introduction to the architectures and algorithms that are used in machine learning, to give a basic understanding for the needs that memory devices and their connections need to fulfil.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must

Judgement and approach
For a passing grade the student must


Memory devices of the computer: SRAM, DRAM, NAND

Non-volatile memory devices: The memristor. Resistive memories (RRAM), phase change memories (PCM), ferroelectric memories (FeRAM), magnetic memories (MRAM).

Integration of memory devices: 3D stacking for scalability, crossbar architecture

Neural network architectures: Fully connected networks, convolutional networks, recursive networks. spiking networks.

Machine learning methods: Backpropagation, gradient descent, STDP.

Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written exam, project work, lab work with report.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Code: 0119. Name: Written Exam.
Credits: 4. Grading scale: TH. Assessment: Exam Contents: Written examination that will cover all the topics of the course.
Code: 0219. Name: Lab Exercise.
Credits: 1. Grading scale: UG. Assessment: Passing grade upon participation in the lab exercise and an approved lab report. Contents: Practical lab exercise with subsequent lab report. Further information: Lab work and report writing can be done either alone or in groups of two.
Code: 0319. Name: Project Assignment.
Credits: 2,5. Grading scale: UG. Assessment: The level of understanding reflected in the written report as well as a control of the written program code will determine whether a passing grade has been achieved. Contents: The project consists of designing and training a neural network to recognize at least 90% of the images from a predefined database. The project should be executed alone or in groups of two persons. The project should result in a written report and working MATLAB code.


Assumed prior knowledge: Basic knowledge in solid state physics.
The number of participants is limited to: No

Reading list

Contact and other information

Course coordinator: Mattias Borg,
Course homepage:
Further information: The course uses Canvas for communication, hand-ins and study materials.