Using a 3D printer, a team of UCLA electrical and computer engineers has created an Artificial Intelligence (AI)-based device that can analyze large volumes of data and identify objects at the actual speed of light.
Deep learning is one of the fastest-growing machine learning methods in the machine learning community and is often used in
- medical image analysis,
- language translation,
- image classification,
- speech recognition as well as addressing more specific tasks, such as
- solving inverse imaging problems.
Traditionally, deep learning systems are implemented to be executed on a computer to digitally learn data representation and abstraction, and perform advanced tasks, comparable to or even better than the performance of humans. However the team led by Dr. Aydogan Ozcan, the Chancellor’s Professor of electrical and computer engineering at UCLA, has introduced a physical mechanism to implement deep learning using an all-optical Diffractive Deep Neural Network (D2NN) / D2N2.
This optical artificial neural network device is intuitively modeled on how the brain processes information. It uses the light bouncing from the object itself to identify that object in as little time as it would take for a computer to simply “see” the object.
The process of creating the artificial neural network began with a computer-simulated design. Then, the researchers used a 3D printer to create very thin, 8 centimeter-square polymer wafers. Each wafer has uneven surfaces, which help diffract light coming from the object in different directions. The layers look opaque to the eye but submillimeter-wavelength terahertz frequencies of light used in the experiments can travel through them. And each layer is composed of tens of thousands of artificial neurons — in this case, tiny pixels that the light travels through.
Together, a series of pixelated layers functions as an “optical network” that shapes how incoming light from the object travels through them. The network identifies an object because the light coming from the object is mostly diffracted toward a single pixel that is assigned to that type of object.
Ozcan Research Group/UCLA
The researchers then trained the network using a computer to identify the objects in front of it by learning the pattern of diffracted light each object produces as the light from that object passes through the device. The “training” used a branch of artificial intelligence called deep learning, in which machines “learn” through repetition and over time as patterns emerge.
“Using passive components that are fabricated layer by layer, and connecting these layers to each other via light diffraction created a unique all-optical platform to perform machine learning tasks at the speed of light,” said Dr. Ozcan.
In their experiments, the researchers demonstrated that the device could accurately identify handwritten numbers and items of clothing — both of which are commonly used tests in artificial intelligence studies. It can also perform the function of an imaging lens at terahertz spectrum.
UCLA researchers believe that new technologies based on the device could be used to speed up data-intensive tasks that involve sorting and identifying objects. For example, a driverless car using the technology could react instantaneously — even faster than it does using current technology — to a stop sign. With a device based on the UCLA system, the car would “read” the sign as soon as the light from the sign hits it, as opposed to having to “wait” for the car’s camera to image the object and then use its computers to figure out what the object is.
Technology based on the invention could also be used in microscopic imaging and medicine, for example, to sort through millions of cells for signs of disease.
Ozcan Research Group/UCLA
Because its components can be created by a 3D printer, the artificial neural network can be made with larger and additional layers, resulting in a device with hundreds of millions of artificial neurons. Those bigger devices could identify many more objects at the same time or perform more complex data analysis. And the components can be made inexpensively — the device created by the UCLA team could be reproduced for less than $50.
The study was published online in Science on July 26. The research was supported by the National Science Foundation and the Howard Hughes Medical Institute.
“This work opens up fundamentally new opportunities to use an artificial intelligence-based passive device to instantaneously analyze data, images and classify objects,” said Dr Ozcan.
Posted in 3D Printing Application
Aug 3, 2018
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