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- W4226209055 abstract "Keyword Spotting nowadays is an integral part of speech-oriented user interaction targeted for smart devices. To this extent, neural networks are extensively used for their flexibility and high accuracy. However, coming up with a suitable configuration for both accuracy requirements and hardware deployment is a challenge. We propose a regression-based network exploration technique that considers the scaling of the network filters ($s$) and quantization ($q$) of the network layers, leading to a friendly and energy-efficient configuration for FPGA hardware implementation. We experiment with different combinations of $mathcal{NN}scriptstylelangle q,,srangle displaystyle$ on the FPGA to profile the energy consumption of the deployed network so that the user can choose the most energy-efficient network configuration promptly. Our accelerator design is deployed on the Xilinx AC 701 platform and has at least 2.1$times$ and 4$times$ improvements on energy and energy efficiency results, respectively, compared to recent hardware implementations for keyword spotting." @default.
- W4226209055 created "2022-05-05" @default.
- W4226209055 creator A5007957361 @default.
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- W4226209055 date "2022-02-04" @default.
- W4226209055 modified "2023-09-29" @default.
- W4226209055 title "A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting" @default.
- W4226209055 doi "https://doi.org/10.48550/arxiv.2202.02361" @default.
- W4226209055 hasPublicationYear "2022" @default.
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