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Heterogeneously integrated Multi- material Photonic Chiplets for Neuromorphic Photonic Transfer Learning AI Engines

Project description

Photonic integrated circuit technology platform for improved transfer learning AI

AI technologies offer substantial benefits for automating, accelerating and maximising the efficiency of repetitive, hazardous or labour-intensive tasks, especially in sectors like manufacturing. However, these technologies are complex to develop, with machine learning in particular requiring significant time and resources. The EU-funded HAETAE project aims to revolutionise computing by developing a multi-material photonic integrated circuit, or PIC, technology platform and a photonic neural network architecture, designed to integrate with transfer learning methods. This approach will involve the creation and combination of advanced materials, optical circuit architectures and printing processes, optimising both efficiency and cost.

Objective

HAETAE targets to establish a novel computing paradigm by developing a multi-material PIC technology platform and align this along photonic Neural Network architectures capable of operating along the principles of Transfer Learning methods. HAETAE will deploy a co-integrated PIC platform that brings together the best-in-class material platforms through micro-transfer-printing and hybrid multi-chiplet bonding and proceeds along the best-in-class linear optical circuit architectures, combining: a) Si/Si3N4/SiGe photonics for high-speed fan-in, weighting and fan-out computational stages, b) InP actives for on-chip amplification, and all-optical non-linearities, for speed- and SNR-enhancement in neuromorphic photonic circuit layouts, c) Si/Si3N4 non-volatile Micro-Electro-Mechanical Systems (MEMS) structures for energy-efficient and non-volatile weighting, d) embedded FPGA-based control plane for the efficient programmability of MEMS and chip-configuration. It aims to finally deliver a Photonic Transfer Learning engine that can support one order of magnitude improvements along all critical performance metrics of AI chipsets: energy efficiency of <19fJ/MAC and on-chip computational power that can scale to ~4.1PMAC/s. HAETAE aims to highlight the versatility and flexibility of its twofold photonic transfer learning accelerator by targeting three discrete application sectors in communications and computing: i) real-time threat detection processor for DC cybersecurity applications, ii) DL and AI computing as a LLM transformer, and iii) an optics-enabled AI-enhanced DSP processor for IM/DD transceivers.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-JU-RIA - HORIZON JU Research and Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-JU-Chips-2024-3-RIA

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Coordinator

ARISTOTELIO PANEPISTIMIO THESSALONIKIS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 600 000,00
Address
KEDEA BUILDING, TRITIS SEPTEMVRIOU, ARISTOTLE UNIVERSITY CAMPUS
546 36 THESSALONIKI
Greece

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Region
Βόρεια Ελλάδα Κεντρική Μακεδονία Θεσσαλονίκη
Activity type
Higher or Secondary Education Establishments
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 600 000,00

Participants (2)

Partners (2)

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