| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Abstract) |
| Titel: | Framework to port neural networks to FPGA, suitable for realtime signal processing |
| Autor/-in: | Welti, Tobias Moser, Aaron Gelke, Hans-Joachim |
| et. al: | No |
| Angaben zur Konferenz: | Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020 |
| Erscheinungsdatum: | 2020 |
| Verlag / Hrsg. Institution: | WEKA |
| Sprache: | Englisch |
| Schlagwörter: | Artificial Intelligence; Convolutional neural network; Native FPGA implementation; Low latency inference; TensorFlow; Keras |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Due to their hardware architecture, Field Programmable Gate Arrays (FPGAs) are optimally suited for the implementation of machine learning algorithms. So far, it is cumbersome to port a neural network (NN) to an FPGA. A frequently used solution is the implementation of NNs using the Open Compute Language (OpenCL) which can be converted to HDL code for use in the FPGA. While OpenCL supports the development of NN algorithms, it also adds unnecessary overhead to the FPGA netlist, limiting the performance of the FPGA. We have developed a framework for the conversion of fully connected, 1D- and 2D-convolutional NN layers to VHDL ode. The framework converts NN models that are trained in TensorFlow or Keras to a synthesizable VHDL code and creates a C model and testbench for verification. This enables nonlinear signal processing with NNs in real-time directly in the FPGA without the use of an embedded CPU. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/19666 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institute of Embedded Systems (InES) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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Welti, T., Moser, A., & Gelke, H.-J. (2020). Framework to port neural networks to FPGA, suitable for realtime signal processing. Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020.
Welti, T., Moser, A. and Gelke, H.-J. (2020) ‘Framework to port neural networks to FPGA, suitable for realtime signal processing’, in Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020. WEKA.
T. Welti, A. Moser, and H.-J. Gelke, “Framework to port neural networks to FPGA, suitable for realtime signal processing,” in Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020, 2020.
WELTI, Tobias, Aaron MOSER und Hans-Joachim GELKE, 2020. Framework to port neural networks to FPGA, suitable for realtime signal processing. In: Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020. Conference paper. WEKA. 2020
Welti, Tobias, Aaron Moser, and Hans-Joachim Gelke. 2020. “Framework to Port Neural Networks to FPGA, Suitable for Realtime Signal Processing.” Conference paper. In Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020. WEKA.
Welti, Tobias, et al. “Framework to Port Neural Networks to FPGA, Suitable for Realtime Signal Processing.” Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020, WEKA, 2020.
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