Record #241947 - GSI Repository

FVLLMONTI

Ferroelectric Vertical Low energy Low latency low volume Modules fOr Neural network Transformers In 3D

Grant period	2021-01-01 - 2025-08-31
Funding body	European Union
Call number	H2020-FETPROACT-2020-01
Grant number	101016776
Identifier	G:(EU-Grant)101016776

Note: In the context of the fourth industrial revolution along with unprecedented growing global interdependencies, an innovative, inclusive and sustainable society is a sound European priority. For many people, the way towards inclusive and sustainable daily life goes through a lightweight in-ear device allowing speech-to-speech translation. Today, such IoT devices require internet connectivity which is proven to be energy inefficient. While machine translation has greatly improved, an embedded lightweight energy-efficient hardware remains elusive because existing solutions based on artificial neural networks (NNs) are computation-intensive and energy-hungry requiring server-based implementations, which also raises data protection and privacy concerns. Today, 2D electronic architectures suffer from "unscalable" interconnects, making it difficult for them to compete with biological neural systems in terms of real-time information-processing capabilities with comparable energy consumption. Recent advances in materials science, device technology and synaptic architectures have the potential to fill this gap with novel disruptive technologies that go beyond conventional CMOS technology. A promising solution comes from vertical nanowire field-effect transistors (VNWFETs) to unlock the full potential of truly 3D neuromorphic computing performance and density. Through actual VNWFETs fabrication setting up a design-technology co-optimization approach, the FVLLMONTI vision is to develop regular 3D stacked hardware layers of NNs empowering the most efficient machine translation thanks to a fine-grain hardware / software co-optimisation. FVLLMONTI consortium is a strong partnership with complementary expertise and extensive track-records in the fields of nanoelectronics, unconventional logic design, reliability, system‐level design, machine translation, cognition sciences. The consortium is composed of 50% of junior researchers and 90% of first-time participants to FETPROACT.

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Record created 2021-10-10, last modified 2023-08-27

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