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首页> 外文期刊>Biomedical Circuits and Systems, IEEE Transactions on >Realizing Low-Energy Classification Systems by Implementing Matrix Multiplication Directly Within an ADC
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Realizing Low-Energy Classification Systems by Implementing Matrix Multiplication Directly Within an ADC

机译：通过直接在ADC内实现矩阵乘法来实现低能耗分类系统

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摘要

In wearable and implantable medical-sensor applications, low-energy classification systems are of importance for deriving high-quality inferences locally within the device. Given that sensor instrumentation is typically followed by A-D conversion, this paper presents a system implementation wherein the majority of the computations required for classification are implemented within the ADC. To achieve this, first an algorithmic formulation is presented that combines linear feature extraction and classification into a single matrix transformation. Second, a matrix-multiplying ADC (MMADC) is presented that enables multiplication between an analog input sample and a digital multiplier, with negligible additional energy beyond that required for A-D conversion. Two systems mapped to the MMADC are demonstrated: an ECG-based cardiac arrhythmia detector; and an image-pixel-based facial gender detector. The RMS error over all multiplication performed, normalized to the RMS of ideal multiplication results is 0.018. Further, compared to idealized versions of conventional systems, the energy savings obtained are estimated to be and , respectively, while achieving similar level of performance.
机译：在可穿戴和可植入医疗传感器应用中，低能量分类系统对于在设备内部局部推导高质量推断非常重要。考虑到传感器仪器通常会进行A-D转换，因此本文提出了一种系统实现，其中分类所需的大部分计算都在ADC内实现。为了实现这一点，首先提出了一种算法公式，该公式将线性特征提取和分类组合到单个矩阵变换中。其次，提出了一种矩阵乘法ADC（MMADC），它能够在模拟输入采样和数字乘法器之间进行乘法运算，而附加的能量却很少，超出了A-D转换所需的能量。演示了映射到MMADC的两个系统：基于ECG的心律失常检测器；以及基于图像像素的面部性别检测器。所执行的所有乘法的RMS误差均化为理想乘法结果的RMS为0.018。此外，与传统系统的理想版本相比，在实现类似性能水平的同时，所获得的节能量估计分别为和。

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