Description du projet
Le potentiel de la plasmonique pour atteindre une informatique neuromorphique plus économe en énergie
L’informatique neuromorphique, qui comprend des dispositifs capables d’imiter les structures biologiques naturelles du système nerveux humain, représente une alternative prometteuse économe en énergie par rapport aux architectures informatiques conventionnelles. Le projet PlasmoniAC, financé par l’UE, investira dans des technologies et des matériaux de première qualité fondés sur la plasmonique afin d’optimiser la puissance de calcul, la taille et l’énergie des puces neuromorphiques. En cas de réussite, le projet fera la démonstration d’une suite neuronale plasmonique, artificielle et puissante. Elle pourrait afficher jusqu’à trois ordres de grandeur supérieurs en matière d’efficacité informatique par neurone, et jusqu’à six ordres de grandeur inférieurs en matière de consommation énergétique par rapport aux meilleures machines neuromorphiques de pointe.
Objectif
PlasmoniAC invests in neuromorphic computing towards sustaining processing power and energy efficiency scaling, adopting the best-in-class material and technology platforms for optimizing computational power, size and energy at every of its constituent functions. It employs the proven high-bandwidth and low-loss credentials of photonic interconnects together with the nm-size memory function of memristor nanoelectronics, bridging them by introducing plasmonics as the ideal technology for offering photonic-level bandwidths and electronic-level footprint computations within ultra-low energy consumption envelopes. Following a holistic hardware/software co-design approach, PlasmoniAC targets the following objectives: i) to elevate plasmonics into a computationally-credible platform with Nx100Gb/s bandwidth, um2-scale size and >1014 MAC/s/W computational energy efficiency, using CMOS compatible BTO and SiOC materials for electro- and thermo-optic computational functions, ii) to blend them via a powerful 3D co-integration platform with SixNy-based photonic interconnects and with non-volatile memristor-based weight control, iii) to fabricate two different sets of 100Gb/s 16- and 8-fan-in linear plasmonic neurons, iv) to deploy a whole new class of plasmo-electronic and nanophotonic activation modules, v) to demonstrate a full-set of sin2(x), ReLU, sigmoid and tanh plasmonic neurons for feed-forward and recurrent neurons, v) to embrace them into a properly adapted Deep Learning training model suite, ultimately delivering a neuromorphic plasmonic software design library, and vi) to apply them on IT security-oriented applications for threat and malware detection. Succeeding in its targets will release a powerful artificial plasmonic neuron suite with up to 3 orders of magnitude higher computational efficiencies per neuron and 1 and 6 orders of magnitude higher energy and footprint efficiencies, respectively, compared to the top state-of-the-art neuromorphic machines.
Champ scientifique
Programme(s)
Régime de financement
RIA - Research and Innovation actionCoordinateur
546 36 THESSALONIKI
Grèce