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High Level Synthesis for Machine Learning

With Deep Learning becoming ubiquitous in our life, running Deep Learning algorithms in real time on an heterogeneous set of hardware platforms is a pressing need in many aspects of our society. While traditional workflows based o... see more

30/09/2022

International Comm...

150K€

Project Budget: 150K€

Project leader

International Committee of the Red Cross No se ha especificado una descripción o un objeto social para esta compañía.

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo H2020 notifico la concesión del proyecto The day 2022-09-30

ERC-2020-POC: Call for proposals for ERC Proof of... Scope:Objectives:

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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2 Participantes

International Committee of t...

150.00K€ | Leader

LIPIDOS SANTIGA

450.12K€ | participant

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Project duration: 18 months Date Start: 2021-03-17
End date: 2022-09-30

Project leader

International Committee of the Red Cross No se ha especificado una descripción o un objeto social para esta compañía.

Project Budget 150K€

participation deadline Sin fecha límite de participación.

Project description With Deep Learning becoming ubiquitous in our life, running Deep Learning algorithms in real time on an heterogeneous set of hardware platforms is a pressing need in many aspects of our society. While traditional workflows based on standard CPUs and GPUs are established, Deep Learning inference on low-power devices (e.g., cars, smart phones, watches, etc) is gaining more attention. Typically, this would require strong background in electronic engineering to convert a neural network into a Digital Signal Processor. We propose to develop a complete open-software library to automatically convert Deep Neural Networks to electronic circuits, using High Level Synthesis tools. With a large basis of potential applications (e.g., autonomous cars, medical devices, portable monitoring devices, custom electronics as in the real-time data processing system of large-scale scientific experiments, etc.), the hls4ml library would assists users by automatising the logic circuit design as well as by reducing resource utilisation while preserving accuracy.

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