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- W4312641555 abstract "The number of CNN-based applications executing at the Edge has been considerably increasing. Considering that CNNs are recognized error-resilient and the varied Edge conditions, we exploit hardware-level Approximate Computing to optimize FPGA-based CNN accelerators without any model retraining or other modifications. Given that, we propose ConfAx, a fully configurable multi-target Framework that navigates the accuracy-performance-resource trade-off to deploy different versions of CNN FPGA approximate accelerators. With an Edge case study (video surveillance), we show that ConfAx reduces power (up to $1.65times)$ and energy (up to $ 1.44times$) over a state-of-the-art accelerator at minor accuracy penalties (0.88% on average)." @default.
- W4312641555 created "2023-01-05" @default.
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- W4312641555 date "2022-05-28" @default.
- W4312641555 modified "2023-09-27" @default.
- W4312641555 title "ConfAx: Exploiting Approximate Computing for Configurable FPGA CNN Acceleration at the Edge" @default.
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- W4312641555 doi "https://doi.org/10.1109/iscas48785.2022.9937676" @default.
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