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• W4280493494 abstract "Optimized model and energy-efficient hardware are both required for deep neural networks (DNNs) in edge-computing area. Neural architecture search (NAS) methods are employed for DNN model optimization with resulted multi-precision networks. Previous works have proposed low-precision-combination (LPC) and high-precision-split (HPS) methods for multi-precision networks, which are not energy-efficient for precision-scalable vector implementation. In this paper, a bit-split-and-combination (BSC) based vector systolic accelerator is developed for a precision-scalable energy-efficient convolution on edge. The maximum energy efficiency of the proposed BSC vector processing element (PE) is up to 1.95× higher in 2-bit, 4-bit and 8-bit operations when compared with LPC and HPS PEs. Further with NAS optimized multi-precision CNN networks, the averaged energy efficiency of the proposed vector systolic BSC PE array achieves up to 2.18× higher in 2-bit, 4-bit and 8-bit operations than that of LPC and HPS PE arrays." @default.
• W4280493494 created "2022-05-22" @default.
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• W4280493494 date "2022-03-14" @default.
• W4280493494 modified "2023-10-18" @default.
• W4280493494 title "A Precision-Scalable Energy-Efficient Bit-Split-and-Combination Vector Systolic Accelerator for NAS-Optimized DNNs on Edge" @default.
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• W4280493494 doi "https://doi.org/10.23919/date54114.2022.9774679" @default.
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