Valid from: Spring 2020
Decided by: Professor Thomas Johansson
Date of establishment: 2020-10-01
Division: Electrical and Information Technology
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: EITP25
Teaching language: 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.
Knowledge and Understanding
For a passing grade the doctoral student must
Competences and Skills
For a passing grade the doctoral student must
Judgement and Approach
For a passing grade the doctoral 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: Feed forward neural networks, recursive networks. spiking neural networks. Machine learning algorithms: Backpropagation, gradient descent, Hebbian and non-Hebbian learning, unsupervised learning through STDP.
Chen, A., Hutchby, J., Zhirnov, V. & Bourianoff, G.: Emerging Nanoelectronic Devices. John Wiley & Sons, 2015. ISBN 9781118447741.
Types of instruction: Lectures, seminars, laboratory exercises, project
Examination formats: Written exam, written report
Grading scale: Failed, pass
Examiner:
Assumed prior knowledge: Basic knowledge of device physics
Course Coordinator: Mattias Borg, mattias.borg@eit.lth.se
Course coordinators: