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- W4308757046 abstract "This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting. It is optimized specifically for low-power processor units like microcontrollers. ML operators exhibit heterogeneous efficiency profiles on power-efficient hardware. Given the exact theoretical computation cost, int8 operators are more computation-effective than float operators, and linear layers are often more efficient than other layers. The proposed LiCo-Net is a dual-phase system that uses the efficient int8 linear operators at the inference phase and applies streaming convolutions at the training phase to maintain a high model capacity. The experimental results show that LiCo-Net outperforms single-value decomposition filter (SVDF) on hardware efficiency with on-par detection performance. Compared to SVDF, LiCo-Net reduces cycles by 40% on HiFi4 DSP." @default.
- W4308757046 created "2022-11-15" @default.
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- W4308757046 date "2022-11-08" @default.
- W4308757046 modified "2023-10-03" @default.
- W4308757046 title "LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting" @default.
- W4308757046 doi "https://doi.org/10.48550/arxiv.2211.04635" @default.
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