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doi:10.22028/D291-46699 | Title: | Deep Neural Network Representation for Explainable Machine Learning Algorithms: A Method for Hardware Acceleration |
| Author(s): | Schauer, Julian Goodarzi, Payman Schütze, Andreas Schneider, Tizian |
| Language: | English |
| Title: | IEEE Instrumentation and Measurement Technology Conference |
| Publisher/Platform: | IEEE |
| Year of Publication: | 2024 |
| Free key words: | edge computing hardware acceleration machine learning neural network |
| DDC notations: | 500 Science |
| Publikation type: | Conference Paper |
| DOI of the first publication: | 10.1109/I2MTC60896.2024.10560978 |
| URL of the first publication: | https://doi.org/10.1109/I2MTC60896.2024.10560978 |
| Link to this record: | urn:nbn:de:bsz:291--ds-466994 hdl:20.500.11880/40942 http://dx.doi.org/10.22028/D291-46699 |
| ISBN: | 979-8-3503-8090-3 |
| Date of registration: | 8-Jan-2026 |
| Faculty: | NT - Naturwissenschaftlich- Technische Fakultät |
| Department: | NT - Systems Engineering |
| Professorship: | NT - Prof. Dr. Andreas Schütze |
| Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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