InVEST Project

Contact

Tim Stadtmann

Name

Tim Stadtmann

Chair of Integrated Digital Systems and Circuit Design

Phone

work
+49 241 80-97578

Email

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International Virtual Engineering Student Teams Project - Enhancing International Experience Through Virtual Teams

InVEST Project Participation

As an InVEST project student, you will be part of a team of 3-4 students from the University of Toronto and RWTH Aachen University. You will work as a team to tackle the project and communicate and collaborate virtually as much as possible. The InVEST program provides value-added services to these projects including access to and training in state-of-the-art virtual collaboration and communication tools, special training sessions on important aspects such as effective team-work and intercultural understanding and communication. The program as a whole is designed to not only enhance technical skills but also teach effective communication and team-work strategies and enhance intercultural understanding in the context of geographically distributed teams. As such, you will be expected to participate in both the technical aspect as well as the intercultural and virtual teamwork value-added activities. These will be executed in 5 sessions of 90 minutes each and 5 hours outside-class activities.

Project Title: Automated Hardware Design Generation of Biological Neuron Models

Project Description:

The project lies within the field of Neuromorphic Computing which can be divided into two application areas: Cognitive Computing and Neuroscience Simulation. Cognitive Computing encompasses the areas of Machine Learning and Artificial Neural Networks, which offer brain-inspired solutions for general purpose applications like pattern detection or robotics. The goal of Neuroscience Simulation is to study and understand principles of biological neural networks in the human brain, like cognition and learning. Focusing on the latter, the simulation of natural density networks at high acceleration with respect to real-time requires large amounts of computation. Dedicated hardware accelerators like Spinnaker, BrainScaleS and Loihi are promising platforms in this regard.

One of the many challenges faced by these accelerators is mapping the various types of neuron models to hardware designs. Finding an efficient and maybe even automatized method of synthesizing designs out of high-level neuron descriptions like in Nest/NestML increases flexibility of future acceleration platforms. Embedding new neuroscience insights concerning neural dynamics into these platforms will then take shorter design cycles than more rigid approaches that employ hard-coded designs.

The wide range of neuron models have varying amounts of complexity, ranging from simple linear differential equations that are analytically solvable to non-linears ones that are not. The key challenge will lie in finding the right trade-off between an easy-to-implement generic method for hardware mapping and the model’s efficiency and speed.

Outcomes:

At the end of the project, solutions would be developed by focusing on analytically solvable neuron models, identifying commonly used ones to derive a common description in C++ that can be translated to hardware by a HLS tool like Vivado HLS. Choosing between different models might be done with simple parameters controlling the design’s functionality, either before hardware synthesis or during runtime. A more advanced solution could be the automated translation of existing descriptions in e.g. C++ or NestML into synthesizable C code for Vivado HLS. Developing these solutions would be useful in the field of robotics and pattern detection.

Requirements:

Program: M.Eng, Computer Engineering (ECE) / Biomedical Engineering / MSc, Computer Science

Technical Expertise Required:

  • Background knowledge in software engineering
  • Interest in neuroscience and artificial neural networks
  • Experience in a programming language

Very good team and communication skills in English

Availability: now until August 2021

Application Deadline: open until filled

Required Documents:

  • CV
  • Copy of your most recent transcript
  • Statement of Interest demonstrating your motivation for pursuing the project and what you will bring to this collaborative project

Interested? Send in your documents via E-Mail with the subject line: "InVEST Project Application"

01/03/2021