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#432181 Putting AI in Your Pocket: MIT Chip Cuts ...
Neural networks are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices.
Smartphones these days are getting truly smart, with ever more AI-powered services like digital assistants and real-time translation. But typically the neural nets crunching the data for these services are in the cloud, with data from smartphones ferried back and forth.
That’s not ideal, as it requires a lot of communication bandwidth and means potentially sensitive data is being transmitted and stored on servers outside the user’s control. But the huge amounts of energy needed to power the GPUs neural networks run on make it impractical to implement them in devices that run on limited battery power.
Engineers at MIT have now designed a chip that cuts that power consumption by up to 95 percent by dramatically reducing the need to shuttle data back and forth between a chip’s memory and processors.
Neural nets consist of thousands of interconnected artificial neurons arranged in layers. Each neuron receives input from multiple neurons in the layer below it, and if the combined input passes a certain threshold it then transmits an output to multiple neurons above it. The strength of the connection between neurons is governed by a weight, which is set during training.
This means that for every neuron, the chip has to retrieve the input data for a particular connection and the connection weight from memory, multiply them, store the result, and then repeat the process for every input. That requires a lot of data to be moved around, expending a lot of energy.
The new MIT chip does away with that, instead computing all the inputs in parallel within the memory using analog circuits. That significantly reduces the amount of data that needs to be shoved around and results in major energy savings.
The approach requires the weights of the connections to be binary rather than a range of values, but previous theoretical work had suggested this wouldn’t dramatically impact accuracy, and the researchers found the chip’s results were generally within two to three percent of the conventional non-binary neural net running on a standard computer.
This isn’t the first time researchers have created chips that carry out processing in memory to reduce the power consumption of neural nets, but it’s the first time the approach has been used to run powerful convolutional neural networks popular for image-based AI applications.
“The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays,” Dario Gil, vice president of artificial intelligence at IBM, said in a statement.
“It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future.”
It’s not just research groups working on this, though. The desire to get AI smarts into devices like smartphones, household appliances, and all kinds of IoT devices is driving the who’s who of Silicon Valley to pile into low-power AI chips.
Apple has already integrated its Neural Engine into the iPhone X to power things like its facial recognition technology, and Amazon is rumored to be developing its own custom AI chips for the next generation of its Echo digital assistant.
The big chip companies are also increasingly pivoting towards supporting advanced capabilities like machine learning, which has forced them to make their devices ever more energy-efficient. Earlier this year ARM unveiled two new chips: the Arm Machine Learning processor, aimed at general AI tasks from translation to facial recognition, and the Arm Object Detection processor for detecting things like faces in images.
Qualcomm’s latest mobile chip, the Snapdragon 845, features a GPU and is heavily focused on AI. The company has also released the Snapdragon 820E, which is aimed at drones, robots, and industrial devices.
Going a step further, IBM and Intel are developing neuromorphic chips whose architectures are inspired by the human brain and its incredible energy efficiency. That could theoretically allow IBM’s TrueNorth and Intel’s Loihi to run powerful machine learning on a fraction of the power of conventional chips, though they are both still highly experimental at this stage.
Getting these chips to run neural nets as powerful as those found in cloud services without burning through batteries too quickly will be a big challenge. But at the current pace of innovation, it doesn’t look like it will be too long before you’ll be packing some serious AI power in your pocket.
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#431906 Low-Cost Soft Robot Muscles Can Lift 200 ...
Jerky mechanical robots are staples of science fiction, but to seamlessly integrate into everyday life they’ll need the precise yet powerful motor control of humans. Now scientists have created a new class of artificial muscles that could soon make that a reality.
The advance is the latest breakthrough in the field of soft robotics. Scientists are increasingly designing robots using soft materials that more closely resemble biological systems, which can be more adaptable and better suited to working in close proximity to humans.
One of the main challenges has been creating soft components that match the power and control of the rigid actuators that drive mechanical robots—things like motors and pistons. Now researchers at the University of Colorado Boulder have built a series of low-cost artificial muscles—as little as 10 cents per device—using soft plastic pouches filled with electrically insulating liquids that contract with the force and speed of mammalian skeletal muscles when a voltage is applied to them.
Three different designs of the so-called hydraulically amplified self-healing electrostatic (HASEL) actuators were detailed in two papers in the journals Science and Science Robotics last week. They could carry out a variety of tasks, from gently picking up delicate objects like eggs or raspberries to lifting objects many times their own weight, such as a gallon of water, at rapid repetition rates.
“We draw our inspiration from the astonishing capabilities of biological muscle,” Christoph Keplinger, an assistant professor at UC Boulder and senior author of both papers, said in a press release. “Just like biological muscle, HASEL actuators can reproduce the adaptability of an octopus arm, the speed of a hummingbird and the strength of an elephant.”
The artificial muscles work by applying a voltage to hydrogel electrodes on either side of pouches filled with liquid insulators, which can be as simple as canola oil. This creates an attraction between the two electrodes, pulling them together and displacing the liquid. This causes a change of shape that can push or pull levers, arms or any other articulated component.
The design is essentially a synthesis of two leading approaches to actuating soft robots. Pneumatic and hydraulic actuators that pump fluids around have been popular due to their high forces, easy fabrication and ability to mimic a variety of natural motions. But they tend to be bulky and relatively slow.
Dielectric elastomer actuators apply an electric field across a solid insulating layer to make it flex. These can mimic the responsiveness of biological muscle. But they are not very versatile and can also fail catastrophically, because the high voltages required can cause a bolt of electricity to blast through the insulator, destroying it. The likelihood of this happening increases in line with the size of their electrodes, which makes it hard to scale them up. By combining the two approaches, researchers get the best of both worlds, with the power, versatility and easy fabrication of a fluid-based system and the responsiveness of electrically-powered actuators.
One of the designs holds particular promise for robotics applications, as it behaves a lot like biological muscle. The so-called Peano-HASEL actuators are made up of multiple rectangular pouches connected in series, which allows them to contract linearly, just like real muscle. They can lift more than 200 times their weight, but being electrically powered, they exceed the flexing speed of human muscle.
As the name suggests, the HASEL actuators are also self-healing. They are still prone to the same kind of electrical damage as dielectric elastomer actuators, but the liquid insulator is able to immediately self-heal by redistributing itself and regaining its insulating properties.
The muscles can even monitor the amount of strain they’re under to provide the same kind of feedback biological systems would. The muscle’s capacitance—its ability to store an electric charge—changes as the device stretches, which makes it possible to power the arm while simultaneously measuring what position it’s in.
The researchers say this could imbue robots with a similar sense of proprioception or body-awareness to that found in plants and animals. “Self-sensing allows for the development of closed-loop feedback controllers to design highly advanced and precise robots for diverse applications,” Shane Mitchell, a PhD student in Keplinger’s lab and an author on both papers, said in an email.
The researchers say the high voltages required are an ongoing challenge, though they’ve already designed devices in the lab that use a fifth of the voltage of those features in the recent papers.
In most of their demonstrations, these soft actuators were being used to power rigid arms and levers, pointing to the fact that future robots are likely to combine both rigid and soft components, much like animals do. The potential applications for the technology range from more realistic prosthetics to much more dextrous robots that can work easily alongside humans.
It will take some work before these devices appear in commercial robots. But the combination of high-performance with simple and inexpensive fabrication methods mean other researchers are likely to jump in, so innovation could be rapid.
Image Credit: Keplinger Research Group/University of Colorado Continue reading
Posted in Human Robots
#431689 Robotic Materials Will Distribute ...
The classical view of a robot as a mechanical body with a central “brain” that controls its behavior could soon be on its way out. The authors of a recent article in Science Robotics argue that future robots will have intelligence distributed throughout their bodies.
The concept, and the emerging discipline behind it, are variously referred to as “material robotics” or “robotic materials” and are essentially a synthesis of ideas from robotics and materials science. Proponents say advances in both fields are making it possible to create composite materials capable of combining sensing, actuation, computation, and communication and operating independently of a central processing unit.
Much of the inspiration for the field comes from nature, with practitioners pointing to the adaptive camouflage of the cuttlefish’s skin, the ability of bird wings to morph in response to different maneuvers, or the banyan tree’s ability to grow roots above ground to support new branches.
Adaptive camouflage and morphing wings have clear applications in the defense and aerospace sector, but the authors say similar principles could be used to create everything from smart tires able to calculate the traction needed for specific surfaces to grippers that can tailor their force to the kind of object they are grasping.
“Material robotics represents an acknowledgment that materials can absorb some of the challenges of acting and reacting to an uncertain world,” the authors write. “Embedding distributed sensors and actuators directly into the material of the robot’s body engages computational capabilities and offloads the rigid information and computational requirements from the central processing system.”
The idea of making materials more adaptive is not new, and there are already a host of “smart materials” that can respond to stimuli like heat, mechanical stress, or magnetic fields by doing things like producing a voltage or changing shape. These properties can be carefully tuned to create materials capable of a wide variety of functions such as movement, self-repair, or sensing.
The authors say synthesizing these kinds of smart materials, alongside other advanced materials like biocompatible conductors or biodegradable elastomers, is foundational to material robotics. But the approach also involves integration of many different capabilities in the same material, careful mechanical design to make the most of mechanical capabilities, and closing the loop between sensing and control within the materials themselves.
While there are stand-alone applications for such materials in the near term, like smart fabrics or robotic grippers, the long-term promise of the field is to distribute decision-making in future advanced robots. As they are imbued with ever more senses and capabilities, these machines will be required to shuttle huge amounts of control and feedback data to and fro, placing a strain on both their communication and computation abilities.
Materials that can process sensor data at the source and either autonomously react to it or filter the most relevant information to be passed on to the central processing unit could significantly ease this bottleneck. In a press release related to an earlier study, Nikolaus Correll, an assistant professor of computer science at the University of Colorado Boulder who is also an author of the current paper, pointed out this is a tactic used by the human body.
“The human sensory system automatically filters out things like the feeling of clothing rubbing on the skin,” he said. “An artificial skin with possibly thousands of sensors could do the same thing, and only report to a central ‘brain’ if it touches something new.”
There are still considerable challenges to realizing this vision, though, the authors say, noting that so far the young field has only produced proof of concepts. The biggest challenge remains manufacturing robotic materials in a way that combines all these capabilities in a small enough package at an affordable cost.
Luckily, the authors note, the field can draw on convergent advances in both materials science, such as the development of new bulk materials with inherent multifunctionality, and robotics, such as the ever tighter integration of components.
And they predict that doing away with the prevailing dichotomy of “brain versus body” could lay the foundations for the emergence of “robots with brains in their bodies—the foundation of inexpensive and ubiquitous robots that will step into the real world.”
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