Abbas Rahimi           

Research Interests:

My research interests lie in codesigning algorithms and emerging hardware systems with an emphasis on improving energy efficiency and robustness. These include brain-inspired hyperdimensional computing, neuro-symbolic AI, distributed intelligent systems, and in general approximation opportunities in computation, communication, sensing, and storage systems.

Short Biography:

I received the B.S. degree in computer engineering from the University of Tehran in 2010, and the M.S. and Ph.D. degrees in computer science and engineering from the University of California San Diego in 2015, followed by postdoctoral researches at the University of California Berkeley, and at the ETH Zürich. In 2020, I have joined the IBM Research-Zürich laboratory as a Research Staff Member.

I received the 2015 Outstanding Dissertation Award in the area of "New Directions in Embedded System Design and Embedded Software'' from the European Design and Automation Association, and the ETH Zürich Postdoctoral Fellowship in 2017. I was a co-recipient of the Best Paper Nominations at DAC (2013) and DATE (2019), and the Best Paper Awards at BICT (2017), BioCAS (2018), and IBM's Pat Goldberg Memorial (2020).

Selected Publications:

• M. Hersche, G. Karunaratne, G. Cherubini, L. Benini, A. Sebastian, A. Rahimi, "Constrained few-shot class-incremental learning",  Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
• G. Karunaratne, M. Hersche, J. Langenegger, G. Cherubini, M. Le Gallo-Bourdeau, U. Egger, K. Brew, S. Choi, I. OK, M. C. Silvestre, N. Li, N. Saulnier, V. Chan, I. Ahsan, V. Narayanan, L. Benini, A. Sebastian, A. Rahimi, "In-memory realization of in-situ few-shot continual learning with a dynamically evolving explicit memory", European Solid State Circuits Conference (ESSCIRC), 2022.
  
• G. Karunaratne, M. Schmuck, M. Le Gallo, G. Cherubini, L. Benini, A. Sebastian, A. Rahimi, "Robust high-dimensional memory-augmented neural networks", Nature Communications, 2021. (Featured in the 50 best articles in the Applied Physics and Mathematics)
  
• M. Hersche, S. Lippuner, M. Korb, L. Benini, A. Rahimi, "Near-channel classifier: symbiotic communication and classification in high-dimensional space", Brain Informatics, 2021. 
  
• A. Moin, A. Zhou, A. Rahimi, et al., "A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition", Nature Electronics, 2021.
• G. Karunaratne, M. Le Gallo, G. Cherubini, L. Benini, A. Rahimi, A. Sebastian, "In-memory hyperdimensional computing", Nature Electronics, 2020. (Appeared on the cover June issue 2020; Received IBM's Pat Goldberg Memorial Best Paper Awards)
  
• A. Burrello, K. Schindler, L. Benini, A. Rahimi, "Hyperdimensional computing with local binary patterns: one-shot learning of seizure onset and identification of ictogenic brain regions using short-time iEEG recordings,” In IEEE Transactions on Biomedical Engineering (TBME), 2020.
  
• M. Schmuck, L. Benini, A. Rahimi, "Hardware optimizations of dense binary hyperdimensional computing: rematerialization of hypervectors, binarized bundling, and combinational associative memory", In ACM Journal on Emerging Technologies in Computing (JETC), 2019.
• A. Rahimi, P. Kanerva, L. Benini, J. M. Rabaey, "Efficient biosignal processing using hyperdimensionalcomputing: network templates for combined learning and classification of ExG signals", In Proceedingsof the IEEE, 2018.
• A. Rahimi, S. Datta, D. Kleyko, E. P. Frady, B. Olshausen, P. Kanerva, J. M. Rabaey, "High-dimensional computing as a nanoscalable paradigm", In IEEE Transactions on Circuits and Systems (TCAS-I), 2017.

Research in the News:

In-memory physical superposition meets few-shot continual learning

Mimicking the brain: Deep learning meets vector-symbolic AI

The best of both worlds: Deep learning meets vector-symbolic architectures

High-five or thumbs-up? New device detects which hand gesture you want to make

Fulfilling Brain-inspired Hyperdimensional Computing with In-memory Computing