Master’s student or intern

Accurate deep learning inference using computational memory

Ref. 2020_027

For decades, conventional computers based on von Neumann architecture have performed computations by repeatedly transferring data between their processing and their memory units, which are physically separated. As computations become increasingly data-centric and as scalability limits in terms of performance and power are being reached, alternative computing paradigms are being sought that collocate computations and storage. A fascinating new approach is that of computational memory where the physics of nanoscale memory devices perform certain computational tasks within the memory unit in a non-von Neumann manner.

Computational memory is finding applications in a variety of application areas such as machine learning and signal processing [1]. Most importantly, it is very appealing for making energy-efficient deep learning inference hardware, where the neural network layers would be encoded in crossbar arrays of memory devices [2]. However, there are several challenges that need to be overcome at both the hardware and the algorithmic level to realize reliable and accurate inference engines based on computational memory.

We are inviting applications from students to conduct their master’s thesis work or an internship project at the IBM Research lab in Zurich on this exciting new topic. The work performed could span low-level hardware experiments on phase-change memory chips comprising more than 1 million devices to high-level algorithmic development in a deep learning framework such as TensorFlow or PyTorch. It also involves interactions with several researchers across IBM research focusing on various aspects of the project. The ideal candidate should have a multi-disciplinary background, strong mathematical aptitude and programming skills. Prior knowledge on emerging memory technologies such as phase-change memory is a bonus but not necessary.

[1] A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh et al.
Memory devices and applications for in-memory computing,”
Nature Nanotechnology (2020).

[2] V. Joshi, M. Le Gallo, S. Haefeli et al.
Accurate deep learning inference using computational phase-change memory,”
Nature Communications (2020).

Diversity

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

How to apply

If you are interested in this challenging position on an exciting new topic, please submit your most recent curriculum vitae including a transcript of grades.

Phase change memory