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Binarized Neural Network for Edge Intelligence of Sensor-Based Human Activity Recognition.pdf
1. Binarized Neural Network for Edge
Intelligence of Sensor
Activity Recognition
Abstract
A wide diversity of sensors has been applied in human activity recognition.
These sensors generate enormous amounts of data during human activity
monitoring. Server-based computing and cloud computing require to upload
all sensor data to servers/clouds for
distance data traveling between sensors and servers increases the costs of
bandwidth and latency. However, human activity recognition has a high
demand for real-time processing. Recently, edge computing is surging to
solve this problem by moving computation and data storage closer to the
sensors, rather than relying on a central server/cloud. Most human activity
recognition is conducted by artificial intelligence, which requires intensive
computation and high power co
for low power, low cost, and low computation. They do not support
computation-intensive deep learning algorithms or result in high latency.
Binarized Neural Network for Edge
Intelligence of Sensor-Based Human
Activity Recognition
A wide diversity of sensors has been applied in human activity recognition.
These sensors generate enormous amounts of data during human activity
based computing and cloud computing require to upload
all sensor data to servers/clouds for data processing and analysis. The long
distance data traveling between sensors and servers increases the costs of
bandwidth and latency. However, human activity recognition has a high
time processing. Recently, edge computing is surging to
solve this problem by moving computation and data storage closer to the
sensors, rather than relying on a central server/cloud. Most human activity
recognition is conducted by artificial intelligence, which requires intensive
computation and high power consumption. Edge servers are usually designed
for low power, low cost, and low computation. They do not support
intensive deep learning algorithms or result in high latency.
Binarized Neural Network for Edge
Based Human
A wide diversity of sensors has been applied in human activity recognition.
These sensors generate enormous amounts of data during human activity
based computing and cloud computing require to upload
data processing and analysis. The long-
distance data traveling between sensors and servers increases the costs of
bandwidth and latency. However, human activity recognition has a high
time processing. Recently, edge computing is surging to
solve this problem by moving computation and data storage closer to the
sensors, rather than relying on a central server/cloud. Most human activity
recognition is conducted by artificial intelligence, which requires intensive
nsumption. Edge servers are usually designed
for low power, low cost, and low computation. They do not support
intensive deep learning algorithms or result in high latency.
2. Fortunately, the development of binarized neural networks enables edge
intelligence, which supports AI running at the network edge for real-time
applications. In this paper, we implement a binarized neural network
( BinaryDilatedDenseNet ) to enable low-latency and low-memory human
activity recognition at the network edge. We applied the
BinaryDilatedDenseNet on three sensor-based human activity recognition
datasets and evaluated it with four metrics. In comparison, the
BinaryDilatedDenseNet outperforms the related work and other three
binarized neural networks in overall and saves 10× memory and 4.5× –
8× inference time compared to the FPDilatedDenseNet(the full-precision
version of the BinaryDilatedDenseNet).