Electronic-photonic Architectures for Brain-inspired Computing

HYBRAIN Scientific papers

These publications have been created with financial support from the European Union’s Horizon Europe research and innovation programme HYBRAIN project under grant agreement No 101046878.

B. Lu, Y. Xia, Y. Ren, M. Xie, L. Zhou, G. Vinai, S. A. Morton, A. T. S. Wee, W. G. van der Wiel, W. Zhang, P. K. J. Wong. "When Machine Learning Meets 2D Materials: A Review" Adv. Sci. 2024, 2305277. https://doi.org/10.1002/advs.202305277

A. Varri, S. Taheriniya, F. Brückerhoff-Plückelmann, I. Bente, N. Farmakidis, D. Bernhardt, H. Rösner, M. Kruth, A. Nadzeyka, T. Richter, C. D. Wright, H. Bhaskaran, G. Wilde, W. H. P. Pernice "Scalable Non-Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation" Adv. Mater. 2023, 2310596. https://doi.org/10.1002/adma.202310596

Alegre-Ibarra et al. "brains-py, A framework to support research on energy-efficient unconventional hardware for machine learning" Journal of Open Source Software, 8(90), 5573 (2023). https://doi.org/10.21105/joss.05573

Dong, B., Aggarwal, S., Zhou, W. et al. "Higher-dimensional processing using a photonic tensor core with continuous-time data" Nat. Photon. 17, 1080–1088 (2023). https://doi.org/10.1038/s41566-023-01313-x

Manuel Le Gallo, Corey Lammie, Julian Büchel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch. "Using the IBM analog in-memory hardware acceleration kit for neural network training and inference" APL Mach. Learn. 1 December 2023; 1 (4): 041102. https://doi.org/10.1063/5.0168089

Jaeger, H., Noheda, B. & van der Wiel, W.G. "Toward a formal theory for computing machines made out of whatever physics offers" Nat Commun 14, 4911 (2023). https://doi.org/10.1038/s41467-023-40533-1

Brückerhoff-Plückelmann, Frank, Bente, Ivonne, Wendland, Daniel, Feldmann, Johannes, Wright, C. David, Bhaskaran, Harish and Pernice, Wolfram. "A large scale photonic matrix processor enabled by charge accumulation" Nanophotonics, vol. 12, no. 5, 2023, pp. 819-825. https://doi.org/10.1515/nanoph-2022-0441

Sarwat, S. G., Zhou, Y., Warner, J., & Bhaskaran, H. (2023). Atomically thin optomemristive feedback neurons." Springer Nature, Nature Nanotechnology, 2023, Issn: 1748-3387. https://doi.org/10.1038/s41565-023-01391-6

Dominique J. Kösters, Bryan A. Kortman, Irem Boybat, Elena Ferro, Sagar Dolas, Roberto Ruiz de Austri, Johan Kwisthout, Hans Hilgenkamp, Theo Rasing, Heike Riel, Abu Sebastian, Sascha Caron, Johan H. Mentink; Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics. APL Machine Learning 1 March 2023; 1 (1): 016101. https://doi.org/10.1063/5.0116699.

Van de Ven B., Alegre-Ibarra U., Lemieszczuk P. J., Bobbert P. A., Ruiz Euler H.-C., van der Wiel W. G. , "Dopant network processing units as tuneable extreme learning machines" Frontiers in Nanotechnology volume 5, 2023, Issn: 2673-3013, doi: 10.3389/fnano.2023.1055527.

J. Büchel et al., "Gradient descent-based programming of analog in-memory computing cores" 2022 International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2022, pp. 33.1.1-33.1.4, doi: 10.1109/IEDM45625.2022.10019486.