逻辑门网络和查找表网络作为轻量级硬件分类器用于患者间心电图心律失常分类
Logic Gate Networks and Lookup Table Networks as Lightweight Hardware Classifiers for Inter-patient ECG Arrhythmia Classification
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中文总结 AI 辅助
本文提出将逻辑门网络和查找表网络作为轻量级硬件分类器，用于患者间心电图心律失常分类，在MIT-BIH数据集上达到94.41%准确率，功耗极低。
中文摘要 AI 辅助
深度可微逻辑门网络（LGNs）和查找表网络（LUTNs）因其使用简单的二进制逻辑运算而非算术运算，为极低功耗推理提供了一种有前景的方法。在本工作中，我们将LGNs的逻辑门推广到多于两个输入引脚，自然得到由N输入查找表（LUTs）组成的网络。为了获得训练N-LUT条目的可微表达式，我们采用2^N:1多路复用器（MUX）的布尔方程，并在训练期间优化其输入参数。我们使用MIT-BIH数据集研究了LGNs和LUTNs在患者间心电图心律失常分类中的适用性。所提出的模型在四分类任务上达到高达94.41%的准确率和0.683的jκ指数，与现有的基于CNN、SVM和SNN的方法相比表现出竞争性能。我们的LGNs和LUTNs仅需估计2.89k至6.17k FLOPs（包括预处理和读出），比最先进方法少三到六个数量级。我们通过在Xilinx Zynq-7000 ZedBoard上实现我们的设计（包括预处理流水线和6-LUTN分类器）进行了验证。整个系统消耗8.25 μJ/推理的动态能量，其中仅0.46 nJ由LUTN分类器利用。这些结果表明，LGNs和LUTNs都可以作为轻量级基于硬件的分类器用于患者间心电图心律失常分类。
英文摘要
Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) offer a promising approach for very low power inference due to their use of simple binary logic operations instead of arithmetic. In this work, we generalize the logic gates of LGNs to more than two input pins, naturally arriving at networks consisting of $N$-input LUTs. To obtain a differentiable expression for training the $N$-LUT entries, we adopt the Boolean equation of a $2^N$:1 multiplexer (MUX) and optimize its input parameters during training. We investigate the applicability of LGNs and LUTNs to inter-patient ECG arrhythmia classification using the MIT-BIH data set. The proposed models achieve up to 94.41\% accuracy and a $jκ$ index of 0.683 on a four-class task, showing a competitive performance compared to existing CNN-, SVM- and SNN-based methods. Our LGNs and LUTNs only require an estimated 2.89k to 6.17k FLOPs, including preprocessing and readout, which is three to six orders of magnitude less than state-of-the-art methods. We verified our design, which consists of the preprocessing pipeline and a 6-LUTN classifier, by implementing it on a Xilinx Zynq-7000 ZedBoard. The complete system consumes a dynamic energy of 8.25 $μ$J/inference, of which only 0.46 nJ is utilized by the LUTN classifier. These results show that both LGNs and LUTNs can be employed as lightweight hardware-based classifiers for inter-patient ECG arrhythmia classification.
