Abstract
We present a new type of 1D-convolutional block allowing us to precompute large parts of a 1D-CNN. The block combines quantization with depthwise-separable convolutions to reduce the overhead for precomputation, making the approach feasible. We present two proof of concept architectures and evaluate them on a Xilinx Spartan-7 S15 low power embedded FPGA. This way we are able to detect atrial fibrillation from 42 s ECG samples. Classifying a sample takes us 0.052 ms, while consuming 0.004 mJ of energy. The networks achieve a classification accuracy of 82.37% and 94.22% respectively. The implementations do not use any block RAM or DSP slices.
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Acknowledgements
The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany in the KI-Sprung LUTNet project (project number 16ES1125).
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Einhaus, L., Qian, C., Ringhofer, C., Schiele, G. (2021). Towards Precomputed 1D-Convolutional Layers for Embedded FPGAs. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1524. Springer, Cham. https://doi.org/10.1007/978-3-030-93736-2_25
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DOI: https://doi.org/10.1007/978-3-030-93736-2_25
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