近年，研究者努力追求更高效的深度神經網絡，尤其是降低乘累加運算
的計算量。傳統如知識蒸餾、剪枝、和量化的策略已被深入挖掘。由於乘
法運算耗能問題，新策略如AdderNet 和ShiftCNN 應運而生，它們目標替
換原有運算，從而節能。
不久前，MADDNESS 提出一全新策略，直接用查找-累加方法取代了乘
累加運算。繼而有如PECAN 和LUT-NN 等研究也秉持此方向。我們的研
究進一步完善了LUT-NN，並提出了端到端的訓練方式。在ImageNet 數據
上的成果表明，我們的方法使LUT-NN 的基礎準確率上升最多至11%。

In recent years, the quest for efficient Deep Neural Networks (DNNs) has centered
on reducing the computational burden of multiply-accumulate (MAC) operations.
Traditional avenues such as Knowledge Distillation (KD), pruning, and
quantization have been explored extensively. With the energy cost of multiplication
operations being a significant concern, alternative methodologies like Adder-
Net and ShiftCNN have emerged, focusing on the direct substitution of operations
to save energy.
Recently, a novel approach called MADDNESS took this further by entirely replacing
MAC operations with lookup-accumulate (LAC) operations. Several subsequent
works, including PECAN and LUT-NN, have followed suit. Our research
builds on and notably improves the latest of these methods, LUT-NN, introducing
an end-to-end training procedure. Tested on the ImageNet dataset, our proposed
method significantly enhances the efficiency of DNNs, improving upon the baseline
LUT-NN model’s accuracy by up to 11%.

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