  Distributed optical fiber vibration sensing (DVS) technology based on
phase-sensitive optical time-domain reflectometry is widely used for safety
monitoring and intrusion event surveillance in wide-ranging fields. Existing
methods rely on deep learning models for event recognition but struggle with
real-time processing of large data volumes in long-distance applications. To
address these challenges, we use a four-layer convolutional neural network
(CNN). The application of knowledge distillation with ResNet as the teacher
model improves the generalization ability of the four-layer CNN, increasing the
accuracy from 83.41% to 95.39% on data from untrained environments. The model
is implemented on a field programmable gate array (FPGA) using a novel design
that replaces multiplication with binary shift operations and quantizes model
weights accordingly, allowing for high parallelism and low latency. An
inference time of 0.083 ms is achieved for a spatial-temporal sample with a
12.5 m fiber length and 0.256 s time frame. This implies the system can process
signals over a fiber length of approximately 38.55 km in real time, which is
more than twice the capability of a GPU of Nvidia GTX 4090. The proposed method
greatly improves the efficiency of vibration pattern recognition, thus
promoting the application of DVS as smart sensing system in various areas. The
data and code is available at https://github.com/HUST-IOF/Efficient-DVS
