The Key Laboratory of Integrated Microsystems (IMS) of Peking University Shenzhen Graduate School has deployed a self-developed acoustic and electromagnetics to artificial intelligence (AETA) system on a large scale and at a high density in China to comprehensively monitor and collect the precursor anomaly signals that occur before earthquakes for seismic prediction. This paper constructs several classic time series and non-time series prediction models for comparison and analysis in order to find the most suitable earthquake-prediction model among these models. The long short-term memory (LSTM) neural network, which gains the best results in earthquake prediction based on AETA data extracted from the precursor anomaly signals, is selected for real-earthquake prediction for 16 consecutive weeks.

Deep convolutional neural networks (CNNs) have shown strong abilities in the application of artificial intelligence. However, due to their extensive amount of computation, traditional processors have low energy efficiency when executing CNN algorithms, which is unacceptable for portable devices with limited hardware cost and battery capacity, so designing a CNN-specific processor is necessary. In this paper, we propose an energy-efficient CNN processor architecture for lightweight devices with a processing elements (PEs) array consisting of 384 PEs. Using the systolic array-based PE array, it realizes parallel operations between filter rows and between channels of output feature maps, supporting the acceleration of 3D convolution and fully connected computation with various parameters by configuring internal instruction registers. The computing strategy based on the proposed systolic dataflow achieves less hardware overhead compared with other strategies, and the reuse of image values and weight values, which effectively reduce the power of memory access. A memory system with a multi-level storage structure combined with register file (RF) and SRAM is used in the proposed CNN processor, which further reduces the energy overhead of computing. The proposed CNN processor architecture has been verified on a ZC706 FPGA platform using VGG-16 based on the proposed image segmentation method, the evaluation results indicate that the peak throughput achieves 115.2 GOP/s consuming 3.801 W at 150 MHz, energy efficiency and DSP efficiency reaches 30.32 GOP/s/W and 0.26 GOP/s/DSP, respectively.