Tag Archives: NVIDIA

#437624 AI-Powered Drone Learns Extreme ...

Posted on November 22, 2020 by Android

Quadrotors are among the most agile and dynamic machines ever created. In the hands of a skilled human pilot, they can do some astonishing series of maneuvers. And while autonomous flying robots have been getting better at flying dynamically in real-world environments, they still haven’t demonstrated the same level of agility of manually piloted ones.

Now researchers from the Robotics and Perception Group at the University of Zurich and ETH Zurich, in collaboration with Intel, have developed a neural network training method that “enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.” Extreme.

There are two notable things here: First, the quadrotor can do these extreme acrobatics outdoors without any kind of external camera or motion-tracking system to help it out (all sensing and computing is onboard). Second, all of the AI training is done in simulation, without the need for an additional simulation-to-real-world (what researchers call “sim-to-real”) transfer step. Usually, a sim-to-real transfer step means putting your quadrotor into one of those aforementioned external tracking systems, so that it doesn’t completely bork itself while trying to reconcile the differences between the simulated world and the real world, where, as the researchers wrote in a paper describing their system, “even tiny mistakes can result in catastrophic outcomes.”

To enable “zero-shot” sim-to-real transfer, the neural net training in simulation uses an expert controller that knows exactly what’s going on to teach a “student controller” that has much less perfect knowledge. That is, the simulated sensory input that the student ends up using as it learns to follow the expert has been abstracted to present the kind of imperfect, imprecise data it’s going to encounter in the real world. This can involve things like abstracting away the image part of the simulation until you’d have no way of telling the difference between abstracted simulation and abstracted reality, which is what allows the system to make that sim-to-real leap.

The simulation environment that the researchers used was Gazebo, slightly modified to better simulate quadrotor physics. Meanwhile, over in reality, a custom 1.5-kilogram quadrotor with a 4:1 thrust to weight ratio performed the physical experiments, using only a Nvidia Jetson TX2 computing board and an Intel RealSense T265, a dual fisheye camera module optimized for V-SLAM. To challenge the learning system, it was trained to perform three acrobatic maneuvers plus a combo of all of them:

Image: University of Zurich/ETH Zurich/Intel

Reference trajectories for acrobatic maneuvers. Top row, from left: Power Loop, Barrel Roll, and Matty Flip. Bottom row: Combo.

All of these maneuvers require high accelerations of up to 3 g’s and careful control, and the Matty Flip is particularly challenging, at least for humans, because the whole thing is done while the drone is flying backwards. Still, after just a few hours of training in simulation, the drone was totally real-world competent at these tricks, and could even extrapolate a little bit to perform maneuvers that it was not explicitly trained on, like doing multiple loops in a row. Where humans still have the advantage over drones is (as you might expect since we’re talking about robots) is quickly reacting to novel or unexpected situations. And when you’re doing this sort of thing outdoors, novel and unexpected situations are everywhere, from a gust of wind to a jealous bird.

For more details, we spoke with Antonio Loquercio from the University of Zurich’s Robotics and Perception Group.

IEEE Spectrum: Can you explain how the abstraction layer interfaces with the simulated sensors to enable effective sim-to-real transfer?

Antonio Loquercio: The abstraction layer applies a specific function to the raw sensor information. Exactly the same function is applied to the real and simulated sensors. The result of the function, which is “abstracted sensor measurements,” makes simulated and real observation of the same scene similar. For example, suppose we have a sequence of simulated and real images. We can very easily tell apart the real from the simulated ones given the difference in rendering. But if we apply the abstraction function of “feature tracks,” which are point correspondences in time, it becomes very difficult to tell which are the simulated and real feature tracks, since point correspondences are independent of the rendering. This applies for humans as well as for neural networks: Training policies on raw images gives low sim-to-real transfer (since images are too different between domains), while training on the abstracted images has high transfer abilities.

How useful is visual input from a camera like the Intel RealSense T265 for state estimation during such aggressive maneuvers? Would using an event camera substantially improve state estimation?

Our end-to-end controller does not require a state estimation module. It shares however some components with traditional state estimation pipelines, specifically the feature extractor and the inertial measurement unit (IMU) pre-processing and integration function. The input of the neural networks are feature tracks and integrated IMU measurements. When looking at images with low features (for example when the camera points to the sky), the neural net will mainly rely on IMU. When more features are available, the network uses to correct the accumulated drift from IMU. Overall, we noticed that for very short maneuvers IMU measurements were sufficient for the task. However, for longer ones, visual information was necessary to successfully address the IMU drift and complete the maneuver. Indeed, visual information reduces the odds of a crash by up to 30 percent in the longest maneuvers. We definitely think that event camera can improve even more the current approach since they could provide valuable visual information during high speed.

“The Matty Flip is probably one of the maneuvers that our approach can do very well … It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.”
—Antonio Loquercio, University of Zurich

You describe being able to train on “maneuvers that stretch the abilities of even expert human pilots.” What are some examples of acrobatics that your drones might be able to do that most human pilots would not be capable of?

The Matty Flip is probably one of the maneuvers that our approach can do very well, but human pilots find very challenging. It basically entails doing a high speed power loop by always looking backward. It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.

What are the limits to the performance of this system?

At the moment the main limitation is the maneuver duration. We never trained a controller that could perform maneuvers longer than 20 seconds. In the future, we plan to address this limitation and train general controllers which can fly in that agile way for significantly longer with relatively small drift. In this way, we could start being competitive against human pilots in drone racing competitions.

Can you talk about how the techniques developed here could be applied beyond drone acrobatics?

The current approach allows us to do acrobatics and agile flight in free space. We are now working to perform agile flight in cluttered environments, which requires a higher degree of understanding of the surrounding with respect to this project. Drone acrobatics is of course only an example application. We selected it because it makes a stress test of the controller performance. However, several other applications which require fast and agile flight can benefit from our approach. Examples are delivery (we want our Amazon packets always faster, don’t we?), search and rescue, or inspection. Going faster allows us to cover more space in less time, saving battery costs. Indeed, agile flight has very similar battery consumption of slow hovering for an autonomous drone.

“Deep Drone Acrobatics,” by Elia Kaufmann, Antonio Loquercio, René Ranftl, Matthias Müller, Vladlen Koltun, and Davide Scaramuzza from the Robotics and Perception Group at the University of Zurich and ETH Zurich, and Intel’s Intelligent Systems Lab, was presented at RSS 2020. Continue reading →

Posted in Human Robots

#437620 The Trillion-Transistor Chip That Just ...

Posted on November 22, 2020 by Android

The history of computer chips is a thrilling tale of extreme miniaturization.

The smaller, the better is a trend that’s given birth to the digital world as we know it. So, why on earth would you want to reverse course and make chips a lot bigger? Well, while there’s no particularly good reason to have a chip the size of an iPad in an iPad, such a chip may prove to be genius for more specific uses, like artificial intelligence or simulations of the physical world.

At least, that’s what Cerebras, the maker of the biggest computer chip in the world, is hoping.

The Cerebras Wafer-Scale Engine is massive any way you slice it. The chip is 8.5 inches to a side and houses 1.2 trillion transistors. The next biggest chip, NVIDIA’s A100 GPU, measures an inch to a side and has a mere 54 billion transistors. The former is new, largely untested and, so far, one-of-a-kind. The latter is well-loved, mass-produced, and has taken over the world of AI and supercomputing in the last decade.

So can Goliath flip the script on David? Cerebras is on a mission to find out.

Big Chips Beyond AI
When Cerebras first came out of stealth last year, the company said it could significantly speed up the training of deep learning models.

Since then, the WSE has made its way into a handful of supercomputing labs, where the company’s customers are putting it through its paces. One of those labs, the National Energy Technology Laboratory, is looking to see what it can do beyond AI.

So, in a recent trial, researchers pitted the chip—which is housed in an all-in-one system about the size of a dorm room mini-fridge called the CS-1—against a supercomputer in a fluid dynamics simulation. Simulating the movement of fluids is a common supercomputer application useful for solving complex problems like weather forecasting and airplane wing design.

The trial was described in a preprint paper written by a team led by Cerebras’s Michael James and NETL’s Dirk Van Essendelft and presented at the supercomputing conference SC20 this week. The team said the CS-1 completed a simulation of combustion in a power plant roughly 200 times faster than it took the Joule 2.0 supercomputer to do a similar task.

The CS-1 was actually faster-than-real-time. As Cerebrus wrote in a blog post, “It can tell you what is going to happen in the future faster than the laws of physics produce the same result.”

The researchers said the CS-1’s performance couldn’t be matched by any number of CPUs and GPUs. And CEO and cofounder Andrew Feldman told VentureBeat that would be true “no matter how large the supercomputer is.” At a point, scaling a supercomputer like Joule no longer produces better results in this kind of problem. That’s why Joule’s simulation speed peaked at 16,384 cores, a fraction of its total 86,400 cores.

A comparison of the two machines drives the point home. Joule is the 81st fastest supercomputer in the world, takes up dozens of server racks, consumes up to 450 kilowatts of power, and required tens of millions of dollars to build. The CS-1, by comparison, fits in a third of a server rack, consumes 20 kilowatts of power, and sells for a few million dollars.

While the task is niche (but useful) and the problem well-suited to the CS-1, it’s still a pretty stunning result. So how’d they pull it off? It’s all in the design.

Cut the Commute
Computer chips begin life on a big piece of silicon called a wafer. Multiple chips are etched onto the same wafer and then the wafer is cut into individual chips. While the WSE is also etched onto a silicon wafer, the wafer is left intact as a single, operating unit. This wafer-scale chip contains almost 400,000 processing cores. Each core is connected to its own dedicated memory and its four neighboring cores.

Putting that many cores on a single chip and giving them their own memory is why the WSE is bigger; it’s also why, in this case, it’s better.

Most large-scale computing tasks depend on massively parallel processing. Researchers distribute the task among hundreds or thousands of chips. The chips need to work in concert, so they’re in constant communication, shuttling information back and forth. A similar process takes place within each chip, as information moves between processor cores, which are doing the calculations, and shared memory to store the results.

It’s a little like an old-timey company that does all its business on paper.

The company uses couriers to send and collect documents from other branches and archives across town. The couriers know the best routes through the city, but the trips take some minimum amount of time determined by the distance between the branches and archives, the courier’s top speed, and how many other couriers are on the road. In short, distance and traffic slow things down.

Now, imagine the company builds a brand new gleaming skyscraper. Every branch is moved into the new building and every worker gets a small filing cabinet in their office to store documents. Now any document they need can be stored and retrieved in the time it takes to step across the office or down the hall to their neighbor’s office. The information commute has all but disappeared. Everything’s in the same house.

Cerebras’s megachip is a bit like that skyscraper. The way it shuttles information—aided further by its specially tailored compiling software—is far more efficient compared to a traditional supercomputer that needs to network a ton of traditional chips.

Simulating the World as It Unfolds
It’s worth noting the chip can only handle problems small enough to fit on the wafer. But such problems may have quite practical applications because of the machine’s ability to do high-fidelity simulation in real-time. The authors note, for example, the machine should in theory be able to accurately simulate the air flow around a helicopter trying to land on a flight deck and semi-automate the process—something not possible with traditional chips.

Another opportunity, they note, would be to use a simulation as input to train a neural network also residing on the chip. In an intriguing and related example, a Caltech machine learning technique recently proved to be 1,000 times faster at solving the same kind of partial differential equations at play here to simulate fluid dynamics.

They also note that improvements in the chip (and others like it, should they arrive) will push back the limits of what can be accomplished. Already, Cerebras has teased the release of its next-generation chip, which will have 2.6 trillion transistors, 850,00 cores, and more than double the memory.

Of course, it still remains to be seen whether wafer-scale computing really takes off. The idea has been around for decades, but Cerebras is the first to pursue it seriously. Clearly, they believe they’ve solved the problem in a way that’s useful and economical.

Other new architectures are also being pursued in the lab. Memristor-based neuromorphic chips, for example, mimic the brain by putting processing and memory into individual transistor-like components. And of course, quantum computers are in a separate lane, but tackle similar problems.

It could be that one of these technologies eventually rises to rule them all. Or, and this seems just as likely, computing may splinter into a bizarre quilt of radical chips, all stitched together to make the most of each depending on the situation.

Image credit: Cerebras Continue reading →

Posted in Human Robots

#437614 Video Friday: Poimo Is a Portable ...

Posted on November 21, 2020 by Android

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today's videos.

Engineers at the University of California San Diego have built a squid-like robot that can swim untethered, propelling itself by generating jets of water. The robot carries its own power source inside its body. It can also carry a sensor, such as a camera, for underwater exploration.

[ UCSD ]

Thanks Ioana!

Shark Robotics, French and European leader in Unmanned Ground Vehicles, is announcing today a disinfection add-on for Boston Dynamics Spot robot, designed to fight the COVID-19 pandemic. The Spot robot with Shark’s purpose-built disinfection payload can decontaminate up to 2,000 m2 in 15 minutes, in any space that needs to be sanitized – such as hospitals, metro stations, offices, warehouses or facilities.

[ Shark Robotics ]

Here’s an update on the Poimo portable inflatable mobility project we wrote about a little while ago; while not strictly robotics, it seems like it holds some promise for rapidly developing different soft structures that robotics might find useful.

[ University of Tokyo ]

Thanks Ryuma!

Pretty cool that you can do useful force feedback teleop while video chatting through a “regular broadband Internet connection.” Although, what “regular” means to you is a bit subjective, right?

[ HEBI Robotics ]

Thanks Dave!

While NASA's Mars rover Perseverance travels through space toward the Red Planet, its nearly identical rover twin is hard at work on Earth. The vehicle system test bed (VSTB) rover named OPTIMISM is a full-scale engineering version of the Mars-bound rover. It is used to test hardware and software before the commands are sent up to the Perseverance rover.

[ NASA ]

Jacquard takes ordinary, familiar objects and enhances them with new digital abilities and experiences, while remaining true to their original purpose — like being your favorite jacket, backpack or a pair of shoes that you love to wear.

Our ambition is simple: to make life easier. By staying connected to your digital world, your things can do so much more. Skip a song by brushing your sleeve. Take a picture by tapping on a shoulder strap. Get reminded about the phone you left behind with a blink of light or a haptic buzz on your cuff.

[ Google ATAP ]

Should you attend the IROS 2020 workshop on “Planetary Exploration Robots: Challenges and Opportunities”? Of course you should!

[ Workshop ]

Kuka makes a lot of these videos where I can’t help but think that if they put as much effort into programming the robot as they did into producing the video, the result would be much more impressive.

[ Kuka ]

The Colorado School of Mines is one of the first customers to buy a Spot robot from Boston Dynamics to help with robotics research. Watch as scientists take Spot into the school's mine for the first time.

[ HCR ] via [ CNET ]

A very interesting soft(ish) actuator from Ayato Kanada at Kyushu University's Control Engineering Lab.

A flexible ultrasonic motor (FUSM), which generates linear motion as a novel soft actuator. This motor consists of a single metal cube stator with a hole and an elastic elongated coil spring inserted into the hole. When voltages are applied to piezoelectric plates on the stator, the coil spring moves back and forward as a linear slider. In the FUSM that uses the friction drive as the principle, the most important parameter for optimizing its output is the preload between the stator and slider. The coil spring has a slightly larger diameter than the stator hole and generates the preload by expanding in a radial direction. The coil springs act not only as a flexible slider but also as a resistive positional sensor. Changes in the resistance between the stator and the coil spring end are converted to a voltage and used for position detection.

[ Control Engineering Lab ]

Thanks Ayato!

We show how to use the limbs of a quadruped robot to identify fine-grained soil, representative for Martian regolith.

[ Paper ] via [ ANYmal Research ]

PR2 is serving breakfast and cleaning up afterwards. It’s slow, but all you have to do is eat and leave.

That poor PR2 is a little more naked than it's probably comfortable with.

[ EASE ]

NVIDIA researchers present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped robot (the Unitree Laikago).

[ NVIDIA ]

What's interesting about this assembly task is that the robot is using its arm only for positioning, and doing the actual assembly with just fingers.

[ RC2L ]

In this electronics assembly application, Kawasaki's cobot duAro2 uses a tool changing station to tackle a multitude of tasks and assemble different CPU models.

Okay but can it apply thermal paste to a CPU in the right way? Personally, I find that impossible.

[ Kawasaki ]

You only need to watch this video long enough to appreciate the concept of putting a robot on a robot.

[ Impress ]

In this lecture, we’ll hear from the man behind one of the biggest robotics companies in the world, Boston Dynamics, whose robotic dog, Spot, has been used to encourage social distancing in Singapore and is now getting ready for FDA approval to be able to measure patients’ vital signs in hospitals.

[ Alan Turing Institute ]

Greg Kahn from UC Berkeley wrote in to share his recent dissertation talk on “Mobile Robot Learning.”

In order to create mobile robots that can autonomously navigate real-world environments, we need generalizable perception and control systems that can reason about the outcomes of navigational decisions. Learning-based methods, in which the robot learns to navigate by observing the outcomes of navigational decisions in the real world, offer considerable promise for obtaining these intelligent navigation systems. However, there are many challenges impeding mobile robots from autonomously learning to act in the real-world, in particular (1) sample-efficiency–how to learn using a limited amount of data? (2) supervision–how to tell the robot what to do? and (3) safety–how to ensure the robot and environment are not damaged or destroyed during learning? In this talk, I will present deep reinforcement learning methods for addressing these real world mobile robot learning challenges and show results which enable ground and aerial robots to navigate in complex indoor and outdoor environments.

[ UC Berkeley ]

Thanks Greg!

Leila Takayama from UC Santa Cruz (and previously Google X and Willow Garage) gives a talk entitled “Toward a more human-centered future of robotics.”

Robots are no longer only in outer space, in factory cages, or in our imaginations. We interact with robotic agents when withdrawing cash from bank ATMs, driving cars with adaptive cruise control, and tuning our smart home thermostats. In the moment of those interactions with robotic agents, we behave in ways that do not necessarily align with the rational belief that robots are just plain machines. Through a combination of controlled experiments and field studies, we use theories and concepts from the social sciences to explore ways that human and robotic agents come together, including how people interact with personal robots and how people interact through telepresence robots. Together, we will explore topics and raise questions about the psychology of human-robot interaction and how we could invent a future of a more human-centered robotics that we actually want to live in.

[ Leila Takayama ]

Roboticist and stand-up comedian Naomi Fitter from Oregon State University gives a talk on “Everything I Know about Telepresence.”

Telepresence robots hold promise to connect people by providing videoconferencing and navigation abilities in far-away environments. At the same time, the impacts of current commercial telepresence robots are not well understood, and circumstances of robot use including internet connection stability, odd personalizations, and interpersonal relationship between a robot operator and people co-located with the robot can overshadow the benefit of the robot itself. And although the idea of telepresence robots has been around for over two decades, available nonverbal expressive abilities through telepresence robots are limited, and suitable operator user interfaces for the robot (for example, controls that allow for the operator to hold a conversation and move the robot simultaneously) remain elusive. So where should we be using telepresence robots? Are there any pitfalls to watch out for? What do we know about potential robot expressivity and user interfaces? This talk will cover my attempts to address these questions and ways in which the robotics research community can build off of this work

[ Talking Robotics ] Continue reading →

Posted in Human Robots

#437407 Nvidia’s Arm Acquisition Brings the ...

Posted on September 21, 2020 by Android

Artificial intelligence and mobile computing have been two of the most disruptive technologies of this century. The unification of the two companies that made them possible could have wide-ranging consequences for the future of computing.

California-based Nvidia’s graphics processing units (GPUs) have powered the deep learning revolution ever since Google researchers discovered in 2011 that they could run neural networks far more efficiently than conventional CPUs. UK company Arm’s energy-efficient chip designs have dominated the mobile and embedded computing markets for even longer.

Now the two will join forces after the American company announced a $40 billion deal to buy Arm from its Japanese owner, Softbank. In a press release announcing the deal, Nvidia touted its potential to rapidly expand the reach of AI into all areas of our lives.

“In the years ahead, trillions of computers running AI will create a new internet-of-things that is thousands of times larger than today’s internet-of-people,” said Nvidia founder and CEO Jensen Huang. “Uniting NVIDIA’s AI computing capabilities with the vast ecosystem of Arm’s CPU, we can advance computing from the cloud, smartphones, PCs, self-driving cars and robotics, to edge IoT, and expand AI computing to every corner of the globe.”

There are good reasons to believe the hype. The two companies are absolutely dominant in their respective fields—Nvidia’s GPUs support more than 97 percent of AI computing infrastructure offered by big cloud service providers, and Arm’s chips power more than 90 percent of smartphones. And there’s little overlap in their competencies, which means the relationship could be a truly symbiotic one.

“I think the deal “fits like a glove” in that Arm plays in areas that Nvidia does not or isn’t that successful, while NVIDIA plays in many places Arm doesn’t or isn’t that successful,” analyst Patrick Moorhead wrote in Forbes.

One of the most obvious directions would be to expand Nvidia’s AI capabilities to the kind of low-power edge devices that Arm excels in. There’s growing demand for AI in devices like smartphones, wearables, cars, and drones, where transmitting data to the cloud for processing is undesirable either for reasons of privacy or speed.

But there might also be fruitful exchanges in the other direction. Huang told Moorhead a major focus would be bringing Arm’s expertise in energy efficiency to the data center. That’s a big concern for technology companies whose electricity bills and green credentials are taking a battering thanks to the huge amounts of energy required to run millions of computer chips around the clock.

The deal may not be plain sailing, though, most notably due to the two companies’ differing business models. While Nvidia sells ready-made processors, Arm simply creates chip designs and then licenses them to other companies who can then customize them to their particular hardware needs. It operates on an open-licence basis whereby any company with the necessary cash can access its designs.

As a result, its designs are found in products built by hundreds of companies that license its innovations, including Apple, Samsung, Huawei, Qualcomm, and even Nvidia. Some, including two of the company’s co-founders, have raised concerns that the purchase by Nvidia, which competes with many of these other companies, could harm the neutrality that has been central to its success.

It’s possible this could push more companies towards RISC-V, an open-source technology developed by researchers at the University of California at Berkeley that rivals Arm’s and is not owned by any one company. However, there are plenty of reasons why most companies still prefer arm over the less feature-rich open-source option, and it might take a considerable push to convince Arm’s customers to jump ship.

The deal will also have to navigate some thorny political issues. Unions, politicians, and business leaders in the UK have voiced concerns that it could lead to the loss of high-tech jobs, and government sources have suggested conditions could be placed on the deal.

Regulators in other countries could also put a spanner in the works. China is concerned that if Arm becomes US-owned, many of the Chinese companies that rely on its technology could become victims of export restrictions as the China-US trade war drags on. South Korea is also wary that the deal could create a new technology juggernaut that could dent Samsung’s growth in similar areas.

Nvidia has made commitments to keep Arm’s headquarters in the UK, which it says should lessen concerns around jobs and export restrictions. It’s also pledged to open a new world-class technology center in Cambridge and build a state-of-the-art AI supercomputer powered by Arm’s chips there. Whether the deal goes through still hangs in the balance, but of it does it could spur a whole new wave of AI innovation.

Image Credit: Nvidia Continue reading →

Posted in Human Robots

#437182 MIT’s Tiny New Brain Chip Aims for AI ...

Posted on June 12, 2020 by Android

The human brain operates on roughly 20 watts of power (a third of a 60-watt light bulb) in a space the size of, well, a human head. The biggest machine learning algorithms use closer to a nuclear power plant’s worth of electricity and racks of chips to learn.

That’s not to slander machine learning, but nature may have a tip or two to improve the situation. Luckily, there’s a branch of computer chip design heeding that call. By mimicking the brain, super-efficient neuromorphic chips aim to take AI off the cloud and put it in your pocket.

The latest such chip is smaller than a piece of confetti and has tens of thousands of artificial synapses made out of memristors—chip components that can mimic their natural counterparts in the brain.

In a recent paper in Nature Nanotechnology, a team of MIT scientists say their tiny new neuromorphic chip was used to store, retrieve, and manipulate images of Captain America’s Shield and MIT’s Killian Court. Whereas images stored with existing methods tended to lose fidelity over time, the new chip’s images remained crystal clear.

“So far, artificial synapse networks exist as software. We’re trying to build real neural network hardware for portable artificial intelligence systems,” Jeehwan Kim, associate professor of mechanical engineering at MIT said in a press release. “Imagine connecting a neuromorphic device to a camera on your car, and having it recognize lights and objects and make a decision immediately, without having to connect to the internet. We hope to use energy-efficient memristors to do those tasks on-site, in real-time.”

A Brain in Your Pocket
Whereas the computers in our phones and laptops use separate digital components for processing and memory—and therefore need to shuttle information between the two—the MIT chip uses analog components called memristors that process and store information in the same place. This is similar to the way the brain works and makes memristors far more efficient. To date, however, they’ve struggled with reliability and scalability.

To overcome these challenges, the MIT team designed a new kind of silicon-based, alloyed memristor. Ions flowing in memristors made from unalloyed materials tend to scatter as the components get smaller, meaning the signal loses fidelity and the resulting computations are less reliable. The team found an alloy of silver and copper helped stabilize the flow of silver ions between electrodes, allowing them to scale the number of memristors on the chip without sacrificing functionality.

While MIT’s new chip is promising, there’s likely a ways to go before memristor-based neuromorphic chips go mainstream. Between now and then, engineers like Kim have their work cut out for them to further scale and demonstrate their designs. But if successful, they could make for smarter smartphones and other even smaller devices.

“We would like to develop this technology further to have larger-scale arrays to do image recognition tasks,” Kim said. “And some day, you might be able to carry around artificial brains to do these kinds of tasks, without connecting to supercomputers, the internet, or the cloud.”

Special Chips for AI
The MIT work is part of a larger trend in computing and machine learning. As progress in classical chips has flagged in recent years, there’s been an increasing focus on more efficient software and specialized chips to continue pushing the pace.

Neuromorphic chips, for example, aren’t new. IBM and Intel are developing their own designs. So far, their chips have been based on groups of standard computing components, such as transistors (as opposed to memristors), arranged to imitate neurons in the brain. These chips are, however, still in the research phase.

Graphics processing units (GPUs)—chips originally developed for graphics-heavy work like video games—are the best practical example of specialized hardware for AI and were heavily used in this generation of machine learning early on. In the years since, Google, NVIDIA, and others have developed even more specialized chips that cater more specifically to machine learning.

The gains from such specialized chips are already being felt.

In a recent cost analysis of machine learning, research and investment firm ARK Invest said cost declines have far outpaced Moore’s Law. In a particular example, they found the cost to train an image recognition algorithm (ResNet-50) went from around $1,000 in 2017 to roughly $10 in 2019. The fall in cost to actually run such an algorithm was even more dramatic. It took $10,000 to classify a billion images in 2017 and just $0.03 in 2019.

Some of these declines can be traced to better software, but according to ARK, specialized chips have improved performance by nearly 16 times in the last three years.

As neuromorphic chips—and other tailored designs—advance further in the years to come, these trends in cost and performance may continue. Eventually, if all goes to plan, we might all carry a pocket brain that can do the work of today’s best AI.

Image credit: Peng Lin Continue reading →

Posted in Human Robots