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New research from UC Berkeley provides a proof of concept for a magnetic switch that could make computers thousands of times more energy-efficient, and provide some amazing new abilities, too.

Computer engineering, but in particular mobile computer engineering, is all about playing a zero-sum game with yourself. Power and efficiency are constantly undercutting one another, creating confounding incentives for designers looking to set records for both talk time and processing speed. At this point it seems obvious that both speed and battery life are limited by the old process of laying down increasingly dense little fields of silicon transistors; whether it’s a quantum computer or a graphene chip, getting more computing power for less electrical power will require a fundamental shift in how we build computers.

A new study from UC Berkeley hopes to provide the basis for just such an advance, laying out their attempt at a silicon replacement they say uses up to 10,000 timesless power than prior solutions. They have designed a system that uses magnetic switches in place of transistors, negating the need for a constant electric current. The idea of a magnetic transistor has been discussed since the early 1990s, but the idea’s downfall has always been the need to create a strong magnetic field to orient the magnets for easy switching; all or most of the power saved by the magnets is spent creating the field needed to actually use those magnets.

This new study, published in Nature, uses a wire made of tantalum, a somewhat rare element used to make capacitors in everything from Blu-Ray players to mobile phones. Tantalum is a good, light-weight conductor, but it has one particularly odd property that’s made it uniquely useful for magnetic applications: when a current flows through the tantalum wire, all clockwise-spinning electrons migrate to one side of the wire, all counter-clockwise-spinning to the other. The physical movement of these electrons creates a polarization in the system — the same sort of polarization prior researchers have had to create with an expensive magnetic field.

If this approach were successful and practical, we could begin to capitalize on some of the shared benefits of all magnetic computing strategies, the most glaring of which is that magnetic switches do not require constant current to maintain their state. Much like a liquid crystal in an e-ink display, a magnetic transistor will maintain its assigned state until actively flipped. This means that a theoretical magnetic processor could use far less energy than semi-conducting silicon ones by accruing energy savings whenever it is not actively doing work. And since tantalum is a fairly well-known material, its incorporation into the manufacturing process shouldn’t prove too difficult.

The Futurist magazine’s top 10 forecasts for 2014 and beyond. 

Every year, the editors of the Futurist magazine identify the most provocative forecasts and statements about the future that we’ve published recently and we put them to into an annual report called “Outlook.” It’s sprawling exploration of what the future looks like at a particular moment in time. To accompany the report, we draft a list of our top 10 favorite predictions from the magazine’s previous 12 months. What are the criteria to be admitted into the top 10? The forecast should be interesting, relatively high impact, and rising in likelihood. In other words, it’s a bit subjective.

There are surely better methods for evaluating statements about the future, but not for our purposes. You see, we aren’t actually interested in attempting to tell our readers what will happen so much as provoking a better discussion about what can happen—and what futures can be avoided, if we discover we’re heading in an unsavory direction.

The future isn’t a destination. But the problem with too many conversations about the future, especially those involving futurists, is that predictions tend to take on unmitigated certainty, sounding like GPS directions. When you reach the Singularity, turn left—that sort of thing. In reality, it’s more like wandering around a city, deciding spur of the moment what road to take.

Is Big Data still a big mystery to you? 

In recent years, the volume of information coming into companies has exploded, so that many IT organizations are dealing with extremely large sets of data. 

IT leaders are rethinking many aspects of how they manage and deliver information, from investments in infrastructure and analytics tools to new policies for organizing and accessing data so they can deliver more of it, faster. They are concerned that if they don't have the right tools and architectures to deal with all that information, then big data can be a big problem. Check out these infographics on Big Data to see the impact...

Earlier this year, NASA, in partnership with Google, acquired the world's largest quantum computer. But just what does the space agency plan to do with a device with such revolutionary potential?

NASA is currently looking at three very basic applications, including one that would serve as a day-planner for busy astronauts who are up in orbit.

"If you're trying to schedule or plan a whole bunch of tasks on the International Space Station, you can do certain tasks only if certain preconditions are met," he explains. "And after you perform the task you end up in another state where you may or may not be able to perform another task. So that's considered a hard optimization problem that a quantum system could potentially solve."

They're also looking to schedule jobs on supercomputers. And in fact, NASA Ames is responsible for running the agency's primary supercomputing facility. No doubt, at any instance of time they've got hundreds of individual jobs running on a supercomputer, while many others are waiting for their turn. A very difficult scenario would involve a job waiting to run — one that requires, say, 500 nodes — on a supercomputer with 1,000 nodes available.

"Which 500 of these 1,000 nodes should we pick to run the job?," he asks. "It's a very difficult scheduling problem."

Another important application is the Kepler search for exoplanets. NASA astronomers use their various telescopes to look at light curves to understand whether any noticeable dimming represents a potential exoplanet as it moves across its host star. This is a massive search problem — one that D-Wave could conceivably help with.

"These are the types of applications that we're trying to run," says Biswas. "We're doing it on our D-Wave system, which is the largest in the world, but it's still not large enough to solve the really hard real world problems. But by tackling the smaller problems, we can extrapolate to how a larger problem could be solved on a larger system." "But each of these images may be at a certain wavelength, and you may not get all the information from the image," he explains. "One of the challenges there is what's called data fusion, where you try to get multiple images and somehow fuse them in some smart way so that you can garner information from a fused image that you couldn't get from a single image.

And at NASA's Ames Research Center in Silicon Valley, Biswas's team runs the supercomputers that power a significant portion of NASA's endeavors, both public and commercial.

"We see quantum computing as a natural extension of our supercomputing efforts," he told me. "In fact, our current belief is that the D-WAVE system and other quantum computers that might come out in the next few years are all going to behave as attached processors to classical silicon computers."

Which is actually quite amazing. So in the future, when a user wants to solve a large problem, they would interact with their usual computer, while certain aspects would be handed over to the quantum computer. After performing the calculation, like an optimization problem, it would send the solution back to the traditional silicon-based machine. It'll be like putting your desktop PC on steroids.

"Just so we're clear, the D-Wave system is just one of many ways to leverage the effects of quantum physics," he told me. "But in order to use any quantum system, the first thing you need to have is a problem mapped in QUBO form." A QUBO form, which stands for a Quadratic Unconstrained Binary Optimization form, is a mathematical representation of any optimization problem that needs to be solved. At this time — and as far as we know — every single quantum computer requires that the input be in QUBO form.

"And that's a serious problem," says Biswas, "because there's no known recipe to devise a problem and then map it into QUBO form. But once we get a QUBO form — which is a graph representation of the problem — we can embed this onto the architecture of the D-Wave machine."

The D-Wave processors run 512 qubits which are made up of 64 unit cells. Each unit cell is made up of 8 qubits. And each qubit is made up of a bipartite graph, so there are four quibits on the left and four on the right. Each of the four qubits are connected to the ones on the right and vice-versa. But it's not a fully connected graph.

"So what happens therefore, is after you take your problem in QUBO form and you try to embed it into the D-WAVE machine it's not a universal quantum computer. It's not like you have computer keyboard and you can just tell the machine what to do." Essentially, the machine becomes dedicated to the task outlined by the QUBO form — a limitation that could impact scalability.

It doesn't take a Watson to realize that even the world's best supercomputers are staggeringly inefficient and energy-intensive machines.

Our brains have upwards of 86 billion neurons, connected by synapses that not only complete myriad logic circuits; they continuously adapt to stimuli, strengthening some connections while weakening others. We call that process learning, and it enables the kind of rapid, highly efficient computational processes that put Siri and Blue Gene to shame.

Materials scientists at the Harvard School of Engineering and Applied Sciences (SEAS) have now created a new type of transistor that mimics the behavior of a synapse. The novel device simultaneously modulates the flow of information in a circuit and physically adapts to changing signals.

Exploiting unusual properties in modern materials, the synaptic transistor could mark the beginning of a new kind of artificial intelligence: one embedded not in smart algorithms but in the very architecture of a computer.

“There’s extraordinary interest in building energy-efficient electronics these days,” says principal investigator Shriram Ramanathan, associate professor of materials science at Harvard SEAS.

“Historically, people have been focused on speed, but with speed comes the penalty of power dissipation. With electronics becoming more and more powerful and ubiquitous, you could have a huge impact by cutting down the amount of energy they consume.”

The human mind, for all its phenomenal computing power, runs on roughly 20 Watts of energy (less than a household light bulb), so it offers a natural model for engineers.

“The transistor we’ve demonstrated is really an analog to the synapse in our brains,” says co-lead author Jian Shi, a postdoctoral fellow at SEAS. “Each time a neuron initiates an action and another neuron reacts, the synapse between them increases the strength of its connection. And the faster the neurons spike each time, the stronger the synaptic connection. Essentially, it memorizes the action between the neurons.”

Magnetic hard discs can store data for little more than a decade. But nanotechnologists have now designed and built a disk that can store data for a million years or more.

Back in 1956, IBM introduced the world’s first commercial computer capable of storing data on a magnetic disk drive. The IBM 305 RAMAC used fifty 24-inch discs to store up to 5 MB, an impressive feat in those days. Today, however, it’s not difficult to find hard drives that can store 1 TB of data on a single 3.5-inch disk. But despite this huge increase in storage density and a similarly impressive improvement in power efficiency, one thing hasn’t changed. The lifetime over which data can be stored on magnetic discs is still about a decade.

That raises an interesting problem. How are we to preserve information about our civilisation on a timescale that outlasts it? In other words, what technology can reliably store information for 1 million years or more?

Today, we get an answer thanks to the work of Jeroen de Vries at the University of Twente in the Netherlands and a few pals. These guys have designed and built a disk capable of storing data over this timescale. And they’ve performed accelerated ageing tests which show it should be able to store data for 1 million years and possibly longer.

These guys start with some theory about aging. Clearly, it’s impractical to conduct an ageing experiment in real time, particularly when the periods involved are measured in millions of years.  But there is a way to accelerate the process of aging.

This is based on the idea that data must be stored in an energy minimum that is separated from other minima by an energy barrier. So to corrupt data by converting a 0 to a 1, for example, requires enough energy to overcome this barrier.

The probability that the system will jump in this way is governed by an idea known as Arrhenius law. This relates the probability of jumping the barrier to factors such as its temperature, the Boltzmann constant and how often a jump can be attempted, which is related to the level of atomic vibrations.

Some straightforward calculations reveal that to last a million years, the required energy barrier is 63 KBT or 70 KBT to last a billion years. “These values are well within the range of today’s technology,” say de Vries and co.

The disk is simple in conception. The data is stored in the pattern of lines etched into a thin metal disc and then covered with a protective layer.

The metal in question is tungsten, which they chose because of its high melting temperature (3,422 degrees C) and low thermal expansion coefficient.  The protective layer is silicon nitride (Si3N4) chosen because of its high resistance to fracture and its low thermal expansion coefficient.

The results are impressive. According to Arrhenius law, a disk capable of surviving a million years would have to survive 1 hour at 445 Kelvin, a test that the new disks passed with ease. Indeed, they survived temperatures up to 848 Kelvin, albeit with significant amounts of information loss.

What will the next fifty years bring in the world of social media, mobile, robotics and more? Our fifty year timeline shows you just what could be in store