Technische Universität Dresden

Bernabé Lineares Barranco

Bernabé Lineares Barranco
Contact Bernabé Lineares Barranco​
ORCID

Contact

Prof. Bernabé Linares-Barranco

Instituto de Microelectrónica de Sevilla (IMSE-CNM)
CSIC and Universidad de Sevilla

Email: bernabe@imse-cnm.csic.es

Research Field and Activities

  • Neuromorphic Engineering
  • Analog and Mixed-Signal VLSI Design
  • Memristive Devices & Circuits
  • Machine Learning Hardware Acceleration

Short Biography

 Bernabé Linares-Barranco is a distinguished researcher in neuromorphic engineering and nanoelectronics. He earned his B.S. in Electronic Physics in 1986 and an M.S. in Microelectronics in 1987. He completed his Ph.D. in high-frequency OTA-C oscillator design in 1990.

Since 1991, Dr. Linares-Barranco has been a Tenured Scientist at the Instituto de Microelectrónica de Sevilla (IMSE-CNM-CSIC) in Spain. His research focuses on analog and mixed-signal VLSI design, neuromorphic systems, spiking neural networks, and memristive devices.

He has contributed significantly to the development of dynamic vision sensors and has co-founded two startups, Prophesee SA and GrAI-Matter-Labs SAS, both specializing in neuromorphic hardware solutions. Dr. Linares-Barranco is recognized for his influential publications and advancements in brain-inspired computing architectures.

Publications

  1. G. Galeote-Checca, G. Panuccio, A. Canal-Alonso, T. Serrano-Gotarredona, and B. Linares-Barranco, “Time series segmentation for recognition of epileptiform patterns recorded via Microelectrode Arrays in vitro,” PlosOne <link>. Also available from: https://arxiv.org/abs/2402.08099
  2.   S. Yang, B. Linares-Barranco, Y. Wu, B. Chen, “Self-Supervised High-order Information Bottleneck Learning of Spiking Neural Network for Robust Event-Based Optical Flow Estimation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, in Press.
  3. F. Faramarzi, B. Linares-Barranco, T. Serrano-Gotarredona, “A 128×128 Electronically Multi-Foveated Dynamic Vision Sensor with Real-Time Resolution Reconfiguration,” IEEE Access, vol. 12, pp. 192656-192671, 2024, doi: 10.1109/ACCESS.2024.3519035 <https://ieeexplore.ieee.org/document/10804122>.
  4.  T. Zhang, Q. Tao, B. Liu, A. Grimaldi, E. Raimondo, M. Jiménez, M. J. Avedillo, J. Núñez, B. Linares-Barranco, T. Serrano-Gotarredona, G. Finocchio, and J. Han, “A Review of Ising Machines Implemented in Conventional and Emerging Technologies”, IEEE Trans. on Nanotechnology, in Press: https://ieeexplore.ieee.org/document/10670493
  5.  A. Mehonic, et al., “Roadmap to Neuromorphic Computing with Emerging Technologies,” APL Materials, Vol.12, Issue 10. https://doi.org/10.1063/5.0179424. Also available in:  arXiv:2407.02353v2.
  6. M. Velazquez-Lopez, B. Linares-Barranco, J. Lee, H. Erfanijazi, A. Patiño-Saucedo, M. Sifalakis, F. Catthoor, and K. Myny, “A tunable multi-timescale Indium-Gallium-Zinc-Oxide Thin-Film Transistor neuron towards hybrid solutions for spiking neuromorphic applications,” Nature Communications Engineering, 3, 102 (2024). <www.nature.com/articles/s44172-024-00248-7>
  7.  R. Fiorelli, M. Rajabali, R. Mendez,, A. Kumar, A. Livitenko, T. Serrano–Gotarredona, F. Moradi, J. Akerman, B. Linares-Barranco, and E. Peralias, “Spin Hall Nano-Oscillator Empirical Electrical Model for Optimal On-chip Detector Design,” IEEE Transactions on Electron Devices, vol. 71, No. 8, pp. 4920-4925, Aug. 2024. <https://ieeexplore.ieee.org/document/10555894>.
  8. O. Maher, M. Jiménez, C. Delacour, J. Núñez, M. J. Avedillo, B. Linares-Barranco, A. Todri-Sanial, G. Indiveri, and S. Karg, “A CMOS-compatible oscillation-based VO2 Ising machine solver”, Nature Communications, 15, 3324 (2024). <nature>
  9.  H. Erfanijazi, L. A. Camuñas-Mesa, E. Vianello, T. Serrano-Gotarredona , and B. Linares-Barranco, “A Fully Digital Relaxation-Aware Analog Programming Technique for HfOx RRAM Arrays”, IEEE Trans. on Circuits and Systems, Part II, vol. 71, No. 8, pp. 1549-7747, Aug. 2024. <https://ieeexplore.ieee.org/document/10454578>
  10.  S. Yang, H. Wang, Y. Pang, M. Rahimi Azghadi, B. Linares-Barranco, “NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites,” IEEE Transactions on Biomedical Circuits and Systems, vol. 18, no. 1, pp. 186-199, Feb. 2024.