Movement, pressure, temperature, humidity, sound frequency — sensors now integrated into our daily lives collect an endless stream of data about the way we interact with products and their environments.
From autonomous vehicles to health monitoring devices, the ever-growing number of smart devices and information generated is becoming challenging to manage and more expensive to process.
“This massive amount of data needs to be stored and analyzed, and as a result, real-time processing is critical,” says Afshin Rahimi, an assistant professor of mechanical and aerospace engineering. “We are examining new techniques to accelerate the process.”
Among these techniques is using gateway devices to analyze data with deep learning models. This is called edge computing, and 90 per cent of industrial enterprises will be using it by 2022, according to a report by business consultants Frost & Sullivan.
The term “edge” refers to applying a deep learning model to analyze the data at the edge of a framework — where data is acquired, in contrast to cloud computing, which conducts the analysis on a remote server — where data is usually warehoused.
“Edge devices are especially promising for accelerating deep learning algorithms due to their low-power budget and high efficiency,” says Dr. Rahimi. “It essentially brings data storage closer to the location where it is needed to improve response times and save bandwidth.”
Rahimi and his team are developing and applying computer vision and deep learning-based models using edge computing to analyze video recorded from manufacturing floors for process monitoring and proactive efficiency improvement.