MIT researchers have designed new methods for paring down neural networks so that they’ll run more efficiently on handheld devices. Method for modeling neural networks’ power consumption could help make the systems portable.
In recent years, the best-performing artificial-intelligence systems — in areas such as autonomous driving, speech recognition, computer
vision, and automatic translation — have come courtesy of software systems known as neural networks.
But neural networks take up a lot of memory and consume a lot of power, so they usually run on servers in the cloud, which receive data from desktop or mobile devices and then send back their analyses. Last year, MIT associate professor of electrical engineering and computer science Vivienne Sze and colleagues unveiled a new, energy-efficient computer chip optimized for neural networks, which could enable powerful artificial-intelligence systems to run locally on mobile devices.
Now, Sze and her colleagues have approached the same problem from the opposite direction, with a battery of techniques for designing more energy-efficient neural networks. First, they developed an analytic method that can determine how much power a neural network will consume when run on a particular type of hardware. Then they used the method to evaluate new techniques for paring down neural networks so that they’ll run more efficiently on handheld devices.
The researchers describe the work in a paper they’re presenting next week at the Computer Vision and Pattern Recognition Conference. In the paper, they report that the methods offered as much as a 73 percent reduction in power consumption over the standard implementation of neural networks, and as much as a 43 percent reduction over the best previous method for paring the networks down.
Engineers at MIT have taken a first step in designing a computer chip that uses a fraction of the power of larger drone computers and is tailored for a drone as small as a bottlecap. Method for designing efficient computer chips may get miniature smart drones off the ground.
In recent years, engineers have worked to shrink drone technology, building flying prototypes that are the size of a bumblebee and loaded
with even tinier sensors and cameras. Thus far, they have managed to miniaturize almost every part of a drone, except for the brains of the entire operation
— the computer chip.
Standard computer chips for quadcoptors and other similarly sized drones process an enormous amount of streaming data from cameras and sensors, and interpret that data on the fly to autonomously direct a drone’s pitch, speed, and trajectory. To do so, these computers use between 10 and 30 watts of power, supplied by batteries that would weigh down a much smaller, bee-sized drone.
Now, engineers at MIT have taken a first step in designing a computer chip that uses a fraction of the power of larger drone computers and is tailored for a drone as small as a bottlecap. They will present a new methodology and design, which they call “Navion,” at the Robotics: Science and Systems conference, held this week at MIT.
The team, led by Sertac Karaman, the Class of 1948 Career Development Associate Professor of Aeronautics and Astronautics at MIT, and Vivienne Sze, an associate professor in MIT's Department of Electrical Engineering and Computer Science, developed a low-power algorithm, in tandem with pared-down hardware, to create a specialized computer chip.
The key contribution of their work is a new approach for designing the chip hardware and the algorithms that run on the chip. “Traditionally, an algorithm is designed, and you throw it over to a hardware person to figure out how to map the algorithm to hardware,” Sze says. “But we found by designing the hardware and algorithms together, we can achieve more substantial power savings.”
“We are finding that this new approach to programming robots, which involves thinking about hardware and algorithms jointly, is key to scaling them down ,” Karaman says.The new chip processes streaming images at 20 frames per second and automatically carries out commands to adjust a drone’s orientation in space. The streamlined chip performs all these computations while using just below 2 watts of power — making it an order of magnitude more efficient than current drone-embedded chips.
Karaman, says the team’s design is the first step toward engineering “the smallest intelligent drone that can fly on its own.” He ultimately envisions disaster-response and search-and-rescue missions in which insect-sized drones flit in and out of tight spaces to examine a collapsed structure or look for trapped individuals. Karaman also foresees novel uses in consumer electronics.
“Imagine buying a bottlecap-sized drone that can integrate with your phone, and you can take it out and fit it in your palm,” he says. “If you lift your hand up a little, it would sense that, and start to fly around and film you. Then you open your hand again and it would land on your palm, and you could upload that video to your phone and share it with others.” Karaman and Sze’s co-authors are graduate students Zhengdong Zhang and Amr Suleiman, and research scientist Luca Carlone.
A GelSight sensor attached to a robot’s gripper enables the robot to determine precisely where it has grasped a small screwdriver, removing it from and inserting it back into a slot, even when the gripper screens the screwdriver from the robot’s camera. GelSight technology lets robots gauge objects’ hardness and manipulate small tools.
Eight years ago, Ted Adelson’s research group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled a new sensor
technology, called GelSight, that uses physical contact with an object to provide a remarkably detailed 3-D map of its surface.
Now, by mounting GelSight sensors on the grippers of robotic arms, two MIT teams have given robots greater sensitivity and dexterity. The researchers presented their work in two papers at the International Conference on Robotics and Automation last week.
In one paper, Adelson’s group uses the data from the GelSight sensor to enable a robot to judge the hardness of surfaces it touches — a crucial ability if household robots are to handle everyday objects.
In the other, Russ Tedrake’s Robot Locomotion Group at CSAIL uses GelSight sensors to enable a robot to manipulate smaller objects than was previously possible. The GelSight sensor is, in some ways, a low-tech solution to a difficult problem. It consists of a block of transparent rubber — the “gel” of its name — one face of which is coated with metallic paint. When the paint-coated face is pressed against an object, it conforms to the object’s shape.
The metallic paint makes the object’s surface reflective, so its geometry becomes much easier for computer vision algorithms to infer. Mounted on the sensor opposite the paint-coated face of the rubber block are three colored lights and a single camera.“[The system] has colored lights at different angles, and then it has this reflective material, and by looking at the colors, the computer … can figure out the 3-D shape of what that thing is,” explains Adelson, the John and Dorothy Wilson Professor of Vision Science in the Department of Brain and Cognitive Sciences.
In both sets of experiments, a GelSight sensor was mounted on one side of a robotic gripper, a device somewhat like the head of a pincer, but with flat gripping surfaces rather than pointed tips.