Day: May 20, 2016

The ARES is pretty simple, as cutting-edge energy storage technology goes. A lot of rocks. A few railcars that, if they weren’t traveling up and down the same 5.5-mile track on a Nevada hillside, would probably be hauling ore around a mining operation. Throw in an electric generator, and you’ve got the future of the American energy grid.

Well, one possible future. Energy storage is a hot topic, because federal and state guidelines are fast pushing utility companies to ramp up their use of renewable energy sources. (California’s supply, for example, has to be 50 percent renewable by 2030.)

The problem is, today’s coal and natural gas guzzling grid doesn’t hold onto the electricity it generates. Utilities immediately deliver whatever they produce, and produce exactly what’s needed, from moment to moment.

Renewables, though, don’t work that way. The wind doesn’t always blow, the sun doesn’t always shine. So utilities are in search of ways to store surplus energy when they’ve got it, so they can distribute it later, when it’s needed.

The most “duh” approach to energy storage is very big batteries like the ones Elon Musk peddles, which are poised to become a lot cheaper in the next five years. Pumped hydroelectric facilities are another option. Or you can move compressed air around underground caves. But none of these options has emerged as the best way to fix the grid.

Then there’s rail energy storage, which is about to get its grand debut. In April, the Bureau of Land Management approved an ARES—that’s Advanced Rail Energy Storage—project, conceived by a Santa Barbara-based energy startup called, well, ARES. By 2019, ARES operations head Francesca Cava says, the facility will occupy 106 acres in the excellently-named town of Pahrump, Nevada. By running a train up and down a hill, ARES can help utilities add to and subtract from the grid as needed.

It’s a wonderfully simple idea, a 19th century solution for a 21st century problem, with some help from the abundant natural resource that is gravity. When the local utility’s got surplus electricity, it powers up the electric motors that drag 9,600 tons of rock- and concrete-filled railcars up a 2,000-foot hill. When it’s got a deficit, 9,600 tons of railcar rumble down, and those motors generate electricity via regenerative braking—the same way your Prius does. Effectively, all the energy used to move the train up the hill is stored, and recouped when it comes back down.

ARES may not like the term pilot project—it’s already built a demonstration track in California—but Pahrump’s new $55 million energy storage system has a lot to prove. This first official outing will start small, providing only ancillary services—that is, helping utility companies make relatively minor adjustments in their electricity output and input.

The Nevada project has a power capacity of 50 megawatts and can produce 12.5 megawatt-hours of energy. That’s relatively large, especially compared to a lot of battery storage projects. But it might not be large enough to make money. “Fifty megawatts doesn’t get us to economies of scale,” ARES CEO James Kelly admitted in an interview with UtilityDive. “We are more efficient as we get larger.”

And while the science behind ARES—rocks and gravity, mostly—is low-tech, that doesn’t make for a cheap project. The company needs to pour money into the on-the-ground electronics and telecommunications equipment, and into bringing railcars up to their specifications. Andy Lubershane, a senior analyst with IHS Emerging Energy Research, says the setup is more expensive per kilowatt-hour “than almost anything else on the market today.”

Ravi Manghani, a senior energy storage analyst at GTM Research, urges a “wait and watch approach.” “Any new technology has to go through some big hurdles,” he says.

And yet, the simplicity of this setup is appealing. Train goes up, train comes down. If only you could catch a ride, too—which ARES says is a very bad idea. “This is a different scenario in terms of safety and weight,” Cava says, meaning you can’t just run these trains through DC Metro’s infrastructure. They’re way too heavy. Sorry to say the future of energy storage won’t look like Magic Mountain at all.

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Google has built its own computer chip. And this won’t be the last.

The Internet’s most powerful company sent a few shock waves through the tech world yesterday when it revealed that a new custom-designed chip helps run what is surely the future of its vast online empire: artificial intelligence.

In building its own chip, Google has taken yet another step along a path that has already remade the tech industry in enormous ways. Over the past decade, the company has designed all sorts of new hardware for the massive data centers that underpin its myriad online services, including computer servers, networking gear, and more. As it created services of unprecedented scope and size, it needed a more efficient breed of hardware to run these services. Over the years, so many other Internet giants have followed suit, forcing a seismic shift in the worldwide hardware market.

The effect of Google’s chip extends well beyond the Google empire. It threatens the future of the chip industry itself.

With its new chip, Google’s aim is the same: unprecedented efficiency. To take AI to new heights, it needs a chip that can do more in less time while consuming less power. But the effect of this chip extends well beyond the Google empire. It threatens the future of commercial chip makers like Intel and nVidia—particularly when you consider Google’s vision for the future. According to Urs Hölzle, the man most responsible for the global data center network that underpins the Google empire, this new custom chip is just the first of many.

No, Google will not sell its chips to other companies. It won’t directly compete with Intel or nVidia. But with its massive data centers, Google is by far the largest potential customer for both of those companies. At the same time, as more and more businesses adopt the cloud computing services offered Google, they’ll be buying fewer and fewer servers (and thus chips) of their own, eating even further into the chip market.

Indeed, Google revealed its new chip as a way of promoting the cloud services that let businesses and coders tap into its AI engines and build them into their own applications. As Google tries to sell other companies on the power of its AI, it’s claiming—in rather loud ways—that it boasts the best hardware for running this AI, hardware that no other company has.

Google’s Need for Speed

Google’s new chip is called the Tensor Processing Unit, or TPU. That’s because it helps run TensorFlow, the software engine that drives the Google’s deep neural networks, networks of hardware and software that can learn particular tasks by analyzing vast amounts of data. Other tech giants typically run their deep neural nets with graphics processing units, or GPUs—chips that were originally designed to render images for games and other graphics-heavy applications. These are well-suited to running the types of calculations that drive deep neural networks. But Google says it has built a chip that’s even more efficient.

According to Google, it tailored the TPU specifically to machine learning so that it needs fewer transistors to run each operation. That means it can squeeze more operations into the chip with each passing second.

For now, Google is using both TPUs and GPUs to run its neural nets. Hölzle declined to go into specifics on how exactly Google was using its TPUs, except to say that they handles “part of the computation” needed to drive voice recognition on Android phones. But he said that Google would be releasing a paper describing the benefits of its chip and that Google will continue to design new chips that handle machine learning in other ways. Eventually, it seems, this will push GPUs out of the equation. “They’re already going away a little,” Hölzle says. “The GPU is too general for machine learning. It wasn’t actually built for that.”

That’s not something nVidia wants to hear. As the world’s primary seller of GPUs, nVidia is now pushing to expand its own business into the AI realm. As Hölzle points out, the latest nVidia GPU offers a mode specifically for machine learning. But clearly, Google wants the change to happen faster. Much faster.

The Smartest Chip

In the meantime, other companies, most notably Microsoft, are exploring another breed of chip. The field-programmable gate array, or FPGA, is a chip you can re-program to perform specific tasks. Microsoft has tested FPGAs with machine learning, and Intel, seeing where this market was going, recently acquired a company that sells FPGAs.

Some analysts think that’s the smarter way to go. An FPGA provides far more flexibility, says Patrick Moorhead, the president and principal analyst at Moor Insights and Strategy, a firm that closely follows the chip business. Moorhead wonders if the new Google TPU is “overkill,” pointing out that such a chip takes at least six months to build—a long time in the incredibly competitive marketplace in which the biggest Internet companies compete.

But Google doesn’t want that flexibility. More than anything, it wants speed. Asked why Google built its chip from scratch rather than using an FPGA, Hölzle said: “It’s just much faster.”

Core Business

Hölzle also points out that Google’s chip doesn’t replace CPUs, the central processing units at the heart of every computer server. The search giant still needs these chips to run the tens of thousands of machines in its data centers, and CPUs are Intel’s main business. Still, if Google is willing to build its own chips just for AI, you have to wonder if it would go so far as to design its own CPUs as well.

Hölzle plays down the possibility. “You want to solve problems that are not solved,” he says. In other words, CPUs are a mature technology that pretty much works as it should. But he also said that Google wants healthy competition in the chip market. In other words, it wants to buy from many sellers—not just, say, Intel. After all, more competition means lower prices for Google. As Hölzle explains, expanding its options is why Google is working with the OpenPower Foundation, which seeks to offer chip designs that anyone can use and modify.

That’s a powerful idea, and a potentially powerful threat to the world’s biggest chip makers. According to Shane Rau, an analyst with research firm IDC, Google buys about 5 percent of all server CPUs sold on Earth. Over a recent year-long period, he says, Google bought about 1.2 million chips. And most of those likely came from Intel. (In 2012, Intel exec Diane Bryant told WIRED that Google bought more server chips from Intel than all but five other companies—and those were all companies that sell servers.)

Whatever its plans for the CPU, Google will continue to explore chips specifically suited to machine learning. It will be several years before we really know what works and what doesn’t. After all, neural networks are constantly evolving as well. “We’re learning all the time,” he says. “It’s not clear to me what the final answer is.” And as it learns, you can bet that the world’s chip makers will be watching.

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