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H2020FUN-Comp

H2020: FUN-Comp

Full Name: Functionally scaled computing technology: From novel devices to non-von Neumann architectures and algorithms for a connected intelligent world

Duration: 1/3/2018-1/3/2022

Partners:

  • University of Exeter
  • Thales
  • WWU
  • University of Oxford
  • IBM Research Zurich
  • IMEC
  • C2N-CNRS

Objective:

  • The Fun-COMP project aims to develop a new wave of industry-relevant technologies that will extend the limits facing mainstream processing and storage approaches. We will do this by delivering innovative nanoelectronic and nanophotonic devices and systems that fuse together the core information processing tasks of computing and memory, that incorporate in hardware the ability to learn adapt and evolve, that are designed from the bottom-up to take advantage of the huge benefits, in terms of increases in speed/bandwidth and reduction in power consumption, promised by the emergence of Silicon photonic systems.

INTEC's Role:

  • Intec performs silicon photonics design and fabrication, and investigates self-learning reservoir computing.

Project Web site: https://fun-comp.org/

People involved

Publications in the framework of this project (3)

    International Journals

  1. A. Lugnan, Santiago Garcia-Cuevas Carrillo, C. David Wright, P. Bienstman, Rigorous dynamic model of a silicon ring resonator with phase change material for a neuromorphic node, Optics Express, 30(14), p.25177-25194 doi:10.1364/OE.459364 (2022)  Download this Publication (2.6MB).
  2. Santiago Garcia-Cuevas Carrillo, A. Lugnan, Emanuele Gemo, P. Bienstman, Wolfram H. P. Pernice, Harish Bhaskaran, C. David Wright, System-Level Simulation for Integrated Phase-Change Photonics, Journal of lightwave technology, 39(20), p. 6392 - 6402 doi:10.1109/JLT.2021.3099914 (2021)  Download this Publication (3MB).
      International Conferences

    1. P. Bienstman, A. Lugnan, S. Aggarwal, F. Brückerhoff-Plückelmann, W. Pernice, H. Bhaskaran, C. Ma, S. Sackesyn, E.J.C. Gooskens, S. Masaad, M. Gouda, R. De Prins, Optical computing in silicon photonics: self-adapting ring networks and quantum recurrent neural networks, Natural and Physical Computing (NNPC), Germany, p.1 (2023)  Download this Publication (207KB).