Student project: Efficient Mapping of Spiking Neural Networks into imec’s large-scale neuromorphic processor

At IMEC (Thesis)

Optimization tool for mapping neural networks into imec's homogeneous multicore neuromorphic processor for best load balance, energy efficiency, and latency.


What you will do
In the context of two European research projects (TEMPO/ANDANTE), the imec neuromorphic team is building a scalable neuromorphic processor. Our neuromorphic processor contains many RISC-V cores connected through an interconnected network. One of the challenges in this project is to efficiently map a large-scale neural network into our homogeneous multicore processor. A mapping algorithm defines how to allocate the available resources in each core (memory and processing time) to efficiently distribute the load and therefore reduce the energy and latency of processing. Additionally, it should reduce the distance between the interconnected cores to minimize the data movement among the cores [1][2]. Compared to the previous works, the extra flexibility of our processor intensifies the importance of an efficient mapping algorithm.

The project duration is 12 months. This project should result in a mapping tool/algorithm that generates efficient mapping topologies for given neural networks. The outcome of the project may be published in a high-impact journal.


The application deadline is 30/September/2021. Your application should contain an up-to-date CV, transcripts for both bachelor's and master's courses, and optionally a recommendation letter. A motivation letter and statement of purpose are not mandatory. After the deadline, the short-listed students will be interviewed by the supervisors and human resource management. The project shall start in summer 2021. Please be advised that non-EU/EEA country students that are studying outside of the Netherlands, need to have a work permit to be able to do an internship in the Netherlands.


[1] A. Balaji et al., "Mapping Spiking Neural Networks to Neuromorphic Hardware," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 28, no. 1, pp. 76-86, Jan. 2020, doi: 10.1109/TVLSI.2019.2951493.
[2] Mapping Deep Neural Networks on SpiNNaker2 (


Main tasks:

  • Literature review on state-of-the-art mapping optimization work and relevant algorithms.
  • Finding/designing a suitable algorithm for mapping SNN into imec neuromorphic processor.
  • Implementation of the mapping tool in software (for example Python).
  • Validating the tool by applying it on several large-scale neural networks.
  • Paper writing and documentation in Imec Holst-Centre.

What we do for you
Imec will provide highly engaged project supervision with experts in the neuromorphic domain, compensation for expenses and a desk in the Holst Centre.


Who you are

  • You are an MSc student in Mathematics, Software engineering, or relevant fields.
  • You are available for 12 months.
  • Very good/excellent analytical skills (especially in optimization).
  • Very good/excellent programming skills (preferably in python).
  • Being familiar with neural networks and neuromorphic systems is a plus.
  • Entitled to do an internship in the Netherlands.
  • Motivated student and eager to work independently and expand knowledge in the field.
  • Good written and verbal English skills.


Click on 'apply' to submit your application. You will then be redirected to e-recruiting.


Please be advised that non-EU/EEA country students that are studying outside of the Netherlands, need to have a work-permit to be able to do an internship in the Netherlands.