Neural Networks and Memristive Hardware Accelerators

Module Neural Networks and Memristive Hardware Accelerators (NES-22-E-NNMHA) and (IST-W-22-NNMHA)

Quick overview

Content (videos)
Important notices
OPAL

Lecturers

Dr.-Ing. Richard Schroedter
Dipl.-Ing. Steffen Seitz
Dr.-Ing. Carsten Knoll

Project supervisors

Dr.-Ing. Richard Schroedter,
Dipl.-Ing. Steffen Seitz,
Dr.-Ing. Carsten Knoll,
Dr. Ahmet Samil Demirkol,
Dr. Dimitrios Prousalis,
M.Sc. Adnan Haidar,
Dipl.-Ing. Kilian Göller,

Course description

The lecture is intended for students in the main study (7th semester) of Informationssystemtechnik (IST) and in the master study (1st semester) of Nanoelectronic Systems (NES).

Neural network systems are modeled after the human brain and can accomplish many difficult tasks such as image, speech recognition and generation as well as autonomous driving. Traditional computer hardware is suffering from a Neumann bottleneck, requiring new strategies to handle the enormous growth in data. This can be tackled with the so-called in-memory computing, which uses memory hardware to store and compute data in the same device. One promising solution are novel materials in the nanoscale offering resistors with memory function, so-called memristors.

This course will introduce the basic concepts of machine learning and neural networks for different data types like time series and images. The audience will learn about different network learning methods, optimizers and loss functions and understand the reliance of these methods on vast amounts of data and that computing power is a limiting factor in neural model development.

Then course will bridge from software to hardware accelerators for implementing neural networks specifically based on memristive devices. Basic memristor theory and its application in crossbars, logic circuits and neurons are introduced, and insights into the concepts of crossbar mapping, peripherals, and spiking neural networks are provided.

In addition, the students learn how to apply the python language and how to implement basic neural models in code utilizing ML related python library’s like PyTorch and simulate memristive circuits using LTSpice.

Parallel to the course, students will work on their own semester project using Python or SPICE and present the implementation results at the end of the lecture.

At the end of the semester the knowledge will be examined by an oral or written exam.

Course content

Python course

Important notices

Winter semester 2024/2025:

Please sign up using the OPAL link below.

Enrollment for the lecture

OPAL course

About this page

Richard Schroedter
Last modified: Mar 25, 2025