Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Delafontaine, Thierry | - |
dc.contributor.author | Rosenthal, Matthias | - |
dc.date.accessioned | 2023-11-10T17:34:18Z | - |
dc.date.available | 2023-11-10T17:34:18Z | - |
dc.date.issued | 2023-10-17 | - |
dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/29035 | - |
dc.description.abstract | FPGAs are an ideal platform to accelerate AI processing at the edge. However, simple toolchains are lacking to deploy AI networks directly on the FPGA. We present a framework that allows the design of custom AI accelerators for FPGAs by generating application application-specific HDL code. Unlike other solutions, the framework proposes a dataflow-style hardware mapping at the RTL level that is specific to the neural network and makes optimal use of available resources. We focused on providing an easy-to-use interface and extensible core that allows developers to add custom operators, optimizers, objectives, metrics, and callbacks. | de_CH |
dc.language.iso | en | de_CH |
dc.rights | Not specified | de_CH |
dc.subject | FPGA | de_CH |
dc.subject | AI | de_CH |
dc.subject | Accelerator | de_CH |
dc.subject | Edge AI | de_CH |
dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
dc.title | Efficient mapping of neural networks on FPGA with generated VHDL code | de_CH |
dc.type | Konferenz: Sonstiges | de_CH |
dcterms.type | Text | de_CH |
zhaw.departement | School of Engineering | de_CH |
zhaw.organisationalunit | Institute of Embedded Systems (InES) | de_CH |
zhaw.conference.details | European Conference on EDGE AI Technologies and Applications – EEAI, Athens, Greece, 17-19 October 2023 | de_CH |
zhaw.funding.eu | Not specified | de_CH |
zhaw.originated.zhaw | Yes | de_CH |
zhaw.publication.status | publishedVersion | de_CH |
zhaw.publication.review | Keine Begutachtung | de_CH |
zhaw.author.additional | Yes | de_CH |
zhaw.display.portrait | Yes | de_CH |
Appears in collections: | Publikationen School of Engineering |
Files in This Item:
There are no files associated with this item.
Show simple item record
Delafontaine, T., & Rosenthal, M. (2023, October 17). Efficient mapping of neural networks on FPGA with generated VHDL code. European Conference on EDGE AI Technologies and Applications – EEAI, Athens, Greece, 17-19 October 2023.
Delafontaine, T. and Rosenthal, M. (2023) ‘Efficient mapping of neural networks on FPGA with generated VHDL code’, in European Conference on EDGE AI Technologies and Applications – EEAI, Athens, Greece, 17-19 October 2023.
T. Delafontaine and M. Rosenthal, “Efficient mapping of neural networks on FPGA with generated VHDL code,” in European Conference on EDGE AI Technologies and Applications – EEAI, Athens, Greece, 17-19 October 2023, Oct. 2023.
DELAFONTAINE, Thierry und Matthias ROSENTHAL, 2023. Efficient mapping of neural networks on FPGA with generated VHDL code. In: European Conference on EDGE AI Technologies and Applications – EEAI, Athens, Greece, 17-19 October 2023. Conference presentation. 17 Oktober 2023
Delafontaine, Thierry, and Matthias Rosenthal. 2023. “Efficient Mapping of Neural Networks on FPGA with Generated VHDL Code.” Conference presentation. In European Conference on EDGE AI Technologies and Applications – EEAI, Athens, Greece, 17-19 October 2023.
Delafontaine, Thierry, and Matthias Rosenthal. “Efficient Mapping of Neural Networks on FPGA with Generated VHDL Code.” European Conference on EDGE AI Technologies and Applications – EEAI, Athens, Greece, 17-19 October 2023, 2023.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.