Cells are the fundamental unit of life activities, and the basis of studying life phenomena. It is very important to observe the growth state of yeast cells for exploring the law of life movement, diagnosis and treatment of diseases, drug screening and so on. This study proposes a kind of intelligent low-cost portable cell culture platform using the microfluidic channel and the special machine learning circuit. The platform can independently complete the whole work of living cell culture and monitoring. For realizing the reusable and low-power deep learning circuit, a complement optimization neural network algorithm for hardware optimization and corresponding multi-clock-domain reusable multi-level precision neural network accelerator circuit were proposed, which can reduce the circuit area and power of convolution operation in all precisions by average 18.11% and 23.5% respectively. Besides, a dynamic multi-level precision control method based on the battery level is proposed to dynamically adjust the precision of machine learning operation, in order to balance the working time and segmentation accuracy of the culture platform. In addition, a microcolumns-based three-port input microfluidic structure was designed for better yeast culture effect. The experiment showed that the culture platform can realize yeast cell culture and achieve almost the same segmentation accuracy as the large biological laboratory with low-power and low-cost. Compared with the previous work, the cost of mass production was reduced by 88.95%, and the equipment volume was 27.1% smaller. At the same time, it can achieve the best balance of working time and working accuracy under the condition of limited power of equipment according to the needs of users. INDEX TERMS Microfluidic channel, yeast culture, portable cell culture platform, intelligent, low-power, lab-on-chip.
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.
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