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This AI Supercomputer Has 13.5 Million Cores—and Was Inbuilt Simply Three Days

Synthetic intelligence is on a tear. Machines can communicate, write, play video games, and generate authentic photographs, video, and music. However as AI’s capabilities have grown, so too have its algorithms.

A decade in the past, machine studying algorithms relied on tens of hundreds of thousands of inside connections, or parameters. At this time’s algorithms frequently attain into the a whole bunch of billions and even trillions of parameters. Researchers say scaling up nonetheless yields efficiency features, and fashions with tens of trillions of parameters might arrive briefly order.

To coach fashions that huge, you want highly effective computer systems. Whereas AI within the early 2010s ran on a handful of graphics processing models—laptop chips that excel on the parallel processing essential to AI—computing wants have grown exponentially, and prime fashions now require a whole bunch or hundreds. OpenAI, Microsoft, Meta, and others are constructing devoted supercomputers to deal with the duty, and so they say these AI machines rank among the many quickest on the planet.

However at the same time as GPUs have been essential to AI scaling—Nvidia’s A100, for instance, remains to be one of many quickest, mostly used chips in AI clusters—weirder options designed particularly for AI have popped up lately.

Cerebras affords one such different.

Making a Meal of AI

The scale of a dinner plate—about 8.5 inches to a aspect—the corporate’s Wafer Scale Engine is the largest silicon chip on the planet, boasting 2.6 trillion transistors and 850,000 cores etched onto a single silicon wafer. Every Wafer Scale Engine serves as the guts of the corporate’s CS-2 laptop.

Alone, the CS-2 is a beast, however final yr Cerebras unveiled a plan to hyperlink CS-2s along with an exterior reminiscence system known as MemoryX and a system to attach CS-2s known as SwarmX. The corporate stated the brand new tech may hyperlink as much as 192 chips and prepare fashions two orders of magnitude bigger than at the moment’s largest, most superior AIs.

“The business is transferring previous 1-trillion-parameter fashions, and we’re extending that boundary by two orders of magnitude, enabling brain-scale neural networks with 120 trillion parameters,” Cerebras CEO and cofounder Andrew Feldman stated.

On the time, all this was theoretical. However final week, the firm introduced they’d linked 16 CS-2s collectively right into a world-class AI supercomputer.

Meet Andromeda

The brand new machine, known as Andromeda, has 13.5 million cores able to speeds over an exaflop (one quintillion operations per second) at 16-bit half precision. Because of the distinctive chip at its core, Andromeda isn’t simply in comparison with supercomputers working on extra conventional CPUs and GPUs, however Feldman advised HPC Wire Andromeda is roughly equal to Argonne Nationwide Laboratory’s Polaris supercomputer, which ranks seventeenth quickest on the planet, in response to the newest Top500 checklist.

Along with efficiency, Andromeda’s speedy construct time, price, and footprint are notable. Argonne started putting in Polaris in the summertime of 2021, and the supercomputer went stay a couple of yr later. It takes up 40 racks, the filing-cabinet-like enclosures housing supercomputer elements. By comparability, Andromeda price $35 million—a modest worth for a machine of its energy—took simply three days to assemble, and makes use of a mere 16 racks.

Cerebras examined the system by coaching 5 variations of OpenAI’s giant language mannequin GPT-3 in addition to Eleuther AI’s open supply GPT-J and GPT-NeoX. And in response to Cerebras, maybe crucial discovering is that Andromeda demonstrated what they name “near-perfect linear scaling” of AI workloads for big language fashions. In brief, meaning as extra CS-2s are added, coaching instances lower proportionately.

Sometimes, the corporate stated, as you add extra chips, efficiency features diminish. Cerebras’s WSE chip, alternatively, might show to scale extra effectively as a result of its 850,000 cores are related to one another on the identical piece of silicon. What’s extra, every core has a reminiscence module proper subsequent door. Taken collectively, the chip slashes the period of time spent shuttling knowledge between cores and reminiscence.

“Linear scaling means if you go from one to 2 programs, it takes half as lengthy in your work to be accomplished. That may be a very uncommon property in computing,” Feldman advised HPC Wire. And, he stated, it could actually scale past 16 related programs.

Past Cerebras’s personal testing, the linear scaling outcomes had been additionally demonstrated throughout work at Argonne Nationwide Laboratory the place researchers used Andromeda to coach the GPT-3-XL giant language algorithm on lengthy sequences of the Covid-19 genome.

In fact, although the system might scale past 16 CS-2s, to what diploma linear scaling persists stays to be seen. Additionally, we don’t but know the way Cerebras performs head-to-head towards different AI chips. AI chipmakers like Nvidia and Intel have begun collaborating in common third-party benchmarking by the likes of MLperf. Cerebras has but to participate.

House to Spare

Nonetheless, the strategy does look like carving out its personal area of interest on the planet of supercomputing, and continued scaling in giant language AI is a major use case. Certainly, Feldman advised Wired final yr that the corporate was already speaking to engineers at OpenAI, a frontrunner in giant language fashions. (OpenAI founder, Sam Altman, can be an investor in Cerebras.)

On its launch in 2020, OpenAI’s giant language mannequin GPT-3, modified the sport each by way of efficiency and dimension. Weighing in at 175 billion parameters, it was the largest AI mannequin on the time and shocked researchers with its skills. Since then, language fashions have reached into the trillions of parameters, and bigger fashions could also be forthcoming. There are rumors—simply that, to this point—that OpenAI will launch GPT-4 within the not-too-distant future and it is going to be one other leap from GPT-3. (We’ll have to attend and see on that rely.)

That stated, regardless of their capabilities, giant language fashions are neither excellent nor universally adored. Their flaws embody output that may be false, biased, and offensive. Meta’s Galactica, skilled on scientific texts, is a current instance. Regardless of a dataset one would possibly assume is much less liable to toxicity than coaching on the open web, the mannequin was simply provoked into producing dangerous and inaccurate textual content and pulled down in simply three days. Whether or not researchers can clear up language AI’s shortcomings stays unsure.

However it appears possible that scaling up will proceed till diminishing returns kick in. The following leap may very well be simply across the nook—and we might have already got the {hardware} to make it occur.

Picture Credit score: Cerebras



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