Explore the words cloud of the Fun-COMP project. It provides you a very rough idea of what is the project "Fun-COMP" about.
The following table provides information about the project.
THE UNIVERSITY OF EXETER
|Coordinator Country||United Kingdom [UK]|
|Total cost||3˙996˙951 €|
|EC max contribution||3˙996˙951 € (100%)|
1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
|Duration (year-month-day)||from 2018-03-01 to 2022-02-28|
Take a look of project's partnership.
|1||THE UNIVERSITY OF EXETER||UK (EXETER)||coordinator||607˙766.00|
|2||INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM||BE (LEUVEN)||participant||616˙550.00|
|3||WESTFAELISCHE WILHELMS-UNIVERSITAET MUENSTER||DE (MUENSTER)||participant||611˙000.00|
|4||THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD||UK (OXFORD)||participant||604˙015.00|
|5||THALES SA||FR (COURBEVOIE)||participant||588˙550.00|
|6||IBM RESEARCH GMBH||CH (RUESCHLIKON)||participant||555˙770.00|
|7||CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS||FR (PARIS)||participant||413˙300.00|
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. We will develop basic information processing building blocks that draw inspiration from biological approaches, providing computing primitives that can mimic the essential features of brain-like synapses and neurons to deliver a new foundation for fast, low-power, functionally-scaled computing based around non-von Neumann approaches. We will combine such computing primitives into reconfigurable integrated processing networks that can implement in hardware novel, intelligent, self-learning and adaptive computational approaches - including spiking neural networks, computing-in-memory and autonomous reservoir computing – and that are capable of addressing complex real-world computational problems in fast, energy-efficient ways. We will address the application of our novel technologies to future computing imperatives, including the analysis and exploitation of ‘big data’ and the ubiquity of computing arising from the ‘Internet of Things’. To realise our goals we bring together a world-leading consortium of industrial and academic researchers whose current work in the development of future information processing and storage technologies defines the state-of-the-art.
|Website for access by general public made live||Websites, patent fillings, videos etc.||2020-01-14 14:25:13|
|Second cross-disciplinary training session and associated training webinars/videos||Websites, patent fillings, videos etc.||2020-01-14 14:27:04|
|Article published in popular scientific/technical magazine/website||Websites, patent fillings, videos etc.||2020-01-14 14:25:15|
|Extended unit-cell with WDM capability demonstrated||Documents, reports||2020-01-14 14:26:58|
|Report on fabrication and performance of first-generation basic N-vN unit-cell devices||Documents, reports||2020-01-14 14:26:59|
|Web-based report and animations of N-vN device simulations||Websites, patent fillings, videos etc.||2020-01-14 14:27:01|
|First cross-disciplinary training session and associated training webinars/videos||Websites, patent fillings, videos etc.||2020-01-14 14:26:56|
Take a look to the deliverables list in detail: detailed list of Fun-COMP deliverables.
|year||authors and title||journal||last update|
Syed Ghazi Sarwat, Nathan Youngblood, Yat-Yin Au, Jan A. Mol, C. David Wright, Harish Bhaskaran
Engineering Interface-Dependent Photoconductivity in Ge 2 Sb 2 Te 5 Nanoscale Devices
published pages: 44906-44914, ISSN: 1944-8244, DOI: 10.1021/acsami.8b17602
|ACS Applied Materials & Interfaces 10/51||2019-10-30|
Andrew Katumba, Xin Yin, Joni Dambre, Peter Bienstman
A Neuromorphic Silicon Photonics Nonlinear Equalizer For Optical Communications With Intensity Modulation and Direct Detection
published pages: 2232-2239, ISSN: 0733-8724, DOI: 10.1109/jlt.2019.2900568
|Journal of Lightwave Technology 37/10||2019-10-30|
Syed Ghazi Sarwat, Zengguang Cheng, Nathan Youngblood, Mohd Sharizal Alias, Sapna Sinha, Jamie Warner, Harish Bhaskaran
Strong Opto-Structural Coupling in Low Dimensional GeSe 3 Films
published pages: 7377-7384, ISSN: 1530-6984, DOI: 10.1021/acs.nanolett.9b03039
|Nano Letters 19/10||2019-10-30|
Xuan Li, Nathan Youngblood, Carlos RÃos, Zengguang Cheng, C. David Wright, Wolfram HP Pernice, Harish Bhaskaran
Fast and reliable storage using a 5â€‰â€‰bit, nonvolatile photonic memory cell
published pages: 1, ISSN: 2334-2536, DOI: 10.1364/optica.6.000001
Zengguang Cheng, Carlos RÃos, Nathan Youngblood, C. David Wright, Wolfram H. P. Pernice, Harish Bhaskaran
Device-Level Photonic Memories and Logic Applications Using Phase-Change Materials
published pages: 1802435, ISSN: 0935-9648, DOI: 10.1002/adma.201802435
|Advanced Materials 30/32||2019-10-30|
Delphin Dodane, JÃ©rÃ´me Bourderionnet, Sylvain CombriÃ©, Alfredo de Rossi
Fully embedded photonic crystal cavity with Q=06 million fabricated within a full-process CMOS multiproject wafer
published pages: 20868, ISSN: 1094-4087, DOI: 10.1364/oe.26.020868
|Optics Express 26/16||2019-10-30|
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The information about "FUN-COMP" are provided by the European Opendata Portal: CORDIS opendata.
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