Stanford's hybrid computer

Hybrid Computer Focuses on Image Recognition for Self-Driving Cars

Aug. 31, 2018
Optical-Electric computer powers faster, less-computationally-intensive image processor.

Image recognition for autonomous cars and aerial drones rely on artificial intelligence and the ability to “teach” their onboard computers to recognize objects like a dog, a pedestrian crossing the street, or a stopped car. Unfortunately, computers running AI algorithms are too large and slow for future applications such as cars, drones, and handheld medical devices. Autonomous cars currently on the road almost all carry a relatively large, slow, and energy-intensive computer in the trunk.

To sidestep this limitation, researchers at Stanford University devised a new type of AI camera and computer that classifies images faster and more energy efficiently, and it should be possible to scale it down small enough to be carried in cars and smaller devices. The system is based on a hybrid optical-electrical computer designed specifically for image analysis.

The first layer of the system is an optical computer which does not require the power-intensive mathematics of digital computing. The second layer is a traditional digital computer. The optical computer preprocesses image data, physically filtering it in several ways an electronic computer would otherwise have to do mathematically. Because the filtering happens naturally as light passes through a set of custom optics, this layer needs no power.

The team effectively outsourced some of AI math to the optics. This saves a lot of time and energy that would otherwise be consumed by computation. It also means profoundly fewer calculations, fewer calls to memory, and far less time to complete the process. Millions of calculations are circumvented and it all happens at the speed of light. Having leapfrogged these preprocessing steps, the remaining analysis takes place in the digital computer layer.

The prototype rivals existing electronic-only computers programmed to perform the same calculations in speed and accuracy, but with substantial computational cost savings. And although the prototype, laid out on a lab bench, would hardly be called small, researchers said their system could be miniaturized to fit in a handheld video camera or an aerial drone.

In both simulations and real-world experiments, the system successfully identified airplanes, automobiles, cats, dogs, and more within natural image settings. The team believes a future version of the system would be especially useful in rapid decision-making applications, such as autonomous vehicles. The team is now working to make the optical component do even more of the preprocessing.

Sponsored Recommendations

The entire spectrum of drive technology

June 5, 2024
Read exciting stories about all aspects of maxon drive technology in our magazine.

MONITORING RELAYS — TYPES AND APPLICATIONS

May 15, 2024
Production equipment is expensive and needs to be protected against input abnormalities such as voltage, current, frequency, and phase to stay online and in operation for the ...

Solenoid Valve Mechanics: Understanding Force Balance Equations

May 13, 2024
When evaluating a solenoid valve for a particular application, it is important to ensure that the valve can both remain in state and transition between its de-energized and fully...

Solenoid Valve Basics: What They Are, What They Do, and How They Work

May 13, 2024
A solenoid valve is an electromechanical device used to control the flow of a liquid or gas. It is comprised of two features: a solenoid and a valve. The solenoid is an electric...

Voice your opinion!

To join the conversation, and become an exclusive member of Machine Design, create an account today!