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In recent years, microelectronics technology has entered the era of nanoelectronics/integrated microsystems. System in package (SiP) and system on chip (SoC) are two important technical approaches for the realization of microsystems. Deep learning technology based on neural networks is used in graphics and images. Computer vision and target recognition are widely used. The deep learning technology of convolutional neural network is an important research field in the miniaturization and miniaturization of embedded platforms. How to combine the lightweight neural network with the microsystem to achieve the optimal balance of performance, size, and power consumption is a difficult point. This article introduces a micro-system implementation scheme that combines SiP technology and FPGA-based convolutional neural network. It uses Zynq SoC and FLASH and DDR3 memory as the main components, and uses SiP high-density system packaging technology to integrate. PL end (FPGA) design Convolutional Neural Network, convolutional neural network accelerator, adopt the method of convolution multi-dimensional division and cyclic block to design the accelerator structure, design multiple multiplication and addition parallel computing units to provide the computing power of the system. Improving and accelerating perform on the YOLOv2_Tiny model. The test uses the COCO data set as the training and test samples. The microsystem can accurately identify the target. The volume is only 30 × 30 × 1.2 mm. The performance reaches 22.09GOPs and the power consumption is only 0.81 W under the working frequency of 150 MHz. Multi-objective balance (performance, size and power consumption) of lightweight neural network Microsystems has realized.

Design and exploration of neural network microsystem based on SiP