hls4ml
Financiado
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...
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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Duración del proyecto: 18 meses
Fecha Inicio: 2021-03-17
Fecha Fin: 2022-09-30
Fecha Fin: 2022-09-30
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2022-09-30
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2020-POC:
Call for proposals for ERC Proof of Concept Grant
Cerrada
hace 4 años
Presupuesto
El presupuesto total del proyecto asciende a
150K€
Líder del proyecto
International Committee of the Red Cross
No se ha especificado una descripción o un objeto social para esta compañía.
450.12K€ |
Participante