School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710000, China.
Sensors (Basel). 2019 Nov 21;19(23):5103. doi: 10.3390/s19235103.
This paper proposes a microfluidic lensless-sensing mobile blood-acquisition and analysis system. For a better tradeoff between accuracy and hardware cost, an integer-only quantization algorithm is proposed. Compared with floating-point inference, the proposed quantization algorithm makes a tradeoff that enables miniaturization while maintaining high accuracy. The quantization algorithm allows the convolutional neural network (CNN) inference to be carried out using integer arithmetic and facilitates hardware implementation with area and power savings. A dual configuration register group structure is also proposed to reduce the interval idle time between every neural network layer in order to improve the CNN processing efficiency. We designed a CNN accelerator architecture for the integer-only quantization algorithm and the dual configuration register group and implemented them in field-programmable gate arrays (FPGA). A microfluidic chip and mobile lensless sensing cell image acquisition device were also developed, then combined with the CNN accelerator to build the mobile lensless microfluidic blood image-acquisition and analysis prototype system. We applied the cell segmentation and cell classification CNN in the system and the classification accuracy reached 98.44%. Compared with the floating-point method, the accuracy dropped by only 0.56%, but the area decreased by 45%. When the system is implemented with the maximum frequency of 100 MHz in the FPGA, a classification speed of 17.9 frames per second (fps) can be obtained. The results show that the quantized CNN microfluidic lensless-sensing blood-acquisition and analysis system fully meets the needs of current portable medical devices, and is conducive to promoting the transformation of artificial intelligence (AI)-based blood cell acquisition and analysis work from large servers to portable cell analysis devices, facilitating rapid early analysis of diseases.
本文提出了一种微流控无透镜传感移动采血和分析系统。为了在准确性和硬件成本之间取得更好的权衡，提出了一种仅整数量化算法。与浮点推理相比，所提出的量化算法进行了权衡，使小型化的同时保持高精度。量化算法允许使用整数算法进行卷积神经网络（CNN）推理，并通过节省面积和功率来促进硬件实现。还提出了一种双配置寄存器组结构，以减少每个神经网络层之间的间隔空闲时间，从而提高 CNN 处理效率。我们为仅整数量化算法和双配置寄存器组设计了 CNN 加速器架构，并在现场可编程门阵列（FPGA）中实现了它们。还开发了微流控芯片和移动无透镜传感单元图像采集设备，然后将它们与 CNN 加速器结合，构建移动无透镜微流控血液图像采集和分析原型系统。我们在系统中应用了细胞分割和细胞分类 CNN，分类精度达到 98.44%。与浮点方法相比，精度仅下降 0.56%，但面积减少了 45%。当系统在 FPGA 中以 100 MHz 的最大频率实现时，可获得 17.9 帧每秒（fps）的分类速度。结果表明，量化的 CNN 微流控无透镜传感血液采集和分析系统完全满足当前便携式医疗设备的需求，有利于推动基于人工智能（AI）的血细胞采集和分析工作从大型服务器向便携式细胞分析设备的转变，便于快速早期分析疾病。
