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High Level Synthesis for Machine Learning

Objective

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.

Call for proposal

ERC-2020-PoC
See other projects for this call

Funding Scheme

ERC-POC-LS - ERC Proof of Concept Lump Sum Pilot

Host institution

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH
Address
Esplanade Des Particules 1 Parcelle 11482 De Meyrin Batiment Cadastral 1046
1211 Geneva 23
Switzerland
Activity type
Research Organisations
EU contribution
€ 150 000

Beneficiaries (1)

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH
Switzerland
EU contribution
€ 150 000
Address
Esplanade Des Particules 1 Parcelle 11482 De Meyrin Batiment Cadastral 1046
1211 Geneva 23
Activity type
Research Organisations