.png){kind=icon}
返回
Edge-Intelligence-Based Seismic Event Detection Using a Hardware-Efficient Neural Network With Field-Programmable Gate Array
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2023.3323331.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article presents a neural network model based on edge intelligence for seismic event detection. We implemented the model in hardware using a field-programmable gate array (FPGA) to achieve in-situ detection of seismic events at acquisition nodes or edge nodes. We designed and implemented the model, focusing on its suitability for hardware implementation on FPGA, employing an encoder-decoder structure. The encoder incorporates reparameterization and depthwise separable convolutions. During training, a multibranch structure was employed, which was then converted to an equivalent single-branch structure during inference to reduce model complexity and parameters. The features extracted by the encoder were further learned by the bi-directional long short-term memory (Bi-LSTM) network and then fed into the decoder for classification. We evaluated the model using the stanford earthquake data set (STEAD) and observed a 70% reduction in parameters while achieving comparable detection performance to EQTransformer. Furthermore, the model structure is well-suited for hardware implementation on FPGA. Applying this model to edge devices for seismic event detection can effectively minimize redundant data transmission and enable in-situ quality control.
Keyword:
Computational modeling
Image edge detection
Feature extraction
Data models
Training
Hardware
Field programmable gate arrays
Edge intelligence
field-programmable gate array (FPGA)
hardware efficient
neural network
seismic event detection
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
LCANet: Lightweight Context-Aware Attention Networks for Earthquake Detection and Phase-Picking on IoT Edge DevicesLCANet: 用于IoT边缘设备上的地震检测和相位拾取的轻量级上下文感知注意网络
PREAI
分享收藏分享收藏A Winograd-Based Integrated Photonics Accelerator for Convolutional Neural Networks基于Winograd的卷积神经网络集成光子学加速器
OAAI
OAAI分享收藏STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI斯坦福地震数据集 (STEAD): 人工智能地震信号的全球数据集.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Confirmation that variants in TTI2 are responsible for autosomal recessive intellectual disability
PREAI
分享收藏
查看更多内容