Cerebras launches new AI supercomputing processor with 2.6 trillion transistors

Cerebras Methods has unveiled its new Wafer Scale Engine 2 processor with a record-setting 2.6 trillion transistors and 850,000 AI-optimized cores. It’s constructed for supercomputing duties, and it’s the second one time since 2019 that Los Altos, California-based Cerebras has unveiled a chip this is principally a complete wafer.

Chipmakers generally slice a wafer from a 12-inch-diameter ingot of silicon to procedure in a chip manufacturing facility. As soon as processed, the wafer is sliced into loads of separate chips that can be utilized in digital .

However Cerebras, began by way of SeaMicro founder Andrew Feldman, takes that wafer and makes a unmarried, huge chip out of it. Each and every piece of the chip, dubbed a core, is interconnected in a complicated technique to different cores. The interconnections are designed to stay the entire cores performing at excessive speeds so the transistors can paintings in combination as one.

Two times as just right because the CS-1

Above: Evaluating the CS-1 to the most important GPU.

Symbol Credit score: Cerebras

In 2019, Cerebras may have compatibility 400,000 cores and 1.2 billion transistors on a wafer chip, the CS-1. It used to be constructed with a 16-nanometer production procedure. However the brand new chip is constructed with a high-end 7-nanometer procedure, which means the width between circuits is seven billionths of a meter. With such miniaturization, Cerebras can cram much more transistors in the similar 12-inch wafer, Feldman mentioned. It cuts that round wafer right into a sq. this is 8 inches by way of 8 inches, and ships the instrument in that shape.

“We have now 123 instances extra cores and 1,000 instances extra reminiscence on chip and 12,000 instances extra reminiscence bandwidth and 45,000 instances extra cloth bandwidth,” Feldman mentioned in an interview with VentureBeat. “We have been competitive on scaling geometry, and we made a collection of microarchitecture enhancements.”

Now Cerebras’ WSE-2 chip has greater than two times as many cores and transistors. By means of comparability the most important graphics processing unit (GPU) has handiest 54 billion transistors — 2.55 trillion fewer transistors than the WSE-2. The WSE-2 additionally has 123 instances extra cores and 1,000 instances extra excessive functionality on-chip excessive reminiscence than GPU competition. Most of the Cerebras cores are redundant in case one section fails.

“This can be a nice success, particularly when taking into consideration that the sector’s 0.33 greatest chip is two.55 trillion transistors smaller than the WSE-2,” mentioned Linley Gwennap, predominant analyst at The Linley Staff, in a observation.

Feldman half-joked that this must end up that Cerebras isn’t a one-trick pony.

“What this avoids is the entire complexity of looking to tie in combination a whole lot of little issues,” Feldman mentioned. “When it’s important to construct a cluster of GPUs, it’s important to unfold your fashion throughout a couple of nodes. It’s important to care for instrument reminiscence sizes and reminiscence bandwidth constraints and verbal exchange and synchronization overheads.”

The CS-2’s specifications

Above: TSMC put the CS-1 in a chip museum.

Symbol Credit score: Cerebras

The WSE-2 will energy the Cerebras CS-2, the trade’s quickest AI laptop, designed and optimized for 7 nanometers and past. Manufactured by way of contract producer TSMC, the WSE-2 greater than doubles all functionality traits at the chip — the transistor rely, core rely, reminiscence, reminiscence bandwidth, and upholstery bandwidth — over the primary technology WSE. The result’s that on each functionality metric, the WSE-2 is orders of magnitude higher and extra performant than any competing GPU in the marketplace, Feldman mentioned.

TSMC put the primary WSE-1 chip in a museum of innovation for chip generation in Taiwan.

“Cerebras does ship the cores promised,” Patrick Moorhead, an analyst at Moor Insights & Technique. “What the corporate is handing over is extra alongside the strains of a couple of clusters on a chip. It does seem to provide Nvidia a run for its cash however doesn’t run uncooked CUDA. That has transform slightly of a de facto usual. Nvidia answers are extra versatile in addition to they may be able to have compatibility into just about any server chassis.”

With each element optimized for AI paintings, the CS-2 delivers extra compute functionality at much less area and no more energy than every other device, Feldman mentioned. Relying on workload, from AI to high-performance computing, CS-2 delivers loads or 1000’s of instances extra functionality than legacy possible choices, and it does so at a fragment of the facility draw and area.

A unmarried CS-2 replaces clusters of loads or 1000’s of graphics processing devices (GPUs) that devour dozens of racks, use loads of kilowatts of energy, and take months to configure and program. At handiest 26 inches tall, the CS-2 suits in one-third of a regular datacenter rack.

“Clearly, there are firms and entities enthusiastic about Cerebras’ wafer-scale answer for enormous knowledge units,” mentioned Jim McGregor, predominant analyst at Tirias Analysis, in an e-mail. “However, there are lots of extra alternatives on the undertaking degree for the tens of millions of alternative AI packages and nonetheless alternatives past what Cerebras may deal with, which is why Nvidia has the SuprPod and Selene supercomputers.”

He added, “You additionally must remember the fact that Nvidia is concentrated on the whole lot from AI robotics with Jenson to supercomputers. Cerebras is extra of a distinct segment platform. It is going to take some alternatives however won’t fit the breadth of what Nvidia is concentrated on. But even so, Nvidia is promoting the whole lot they may be able to construct.”

A variety of consumers

Above: Evaluating the brand new Cerebras chip to its rival, the Nvidia A100.

Symbol Credit score: Cerebras

And the corporate has confirmed itself by way of transport the primary technology to consumers. During the last 12 months, consumers have deployed the Cerebras WSE and CS-1, together with Argonne Nationwide Laboratory; Lawrence Livermore Nationwide Laboratory; Pittsburgh Supercomputing Heart (PSC) for its Neocortex AI supercomputer; EPCC, the supercomputing heart on the College of Edinburgh; pharmaceutical chief GlaxoSmithKline; Tokyo Electron Gadgets; and extra. Consumers praising the chip come with the ones at GlaxoSmithKline and the Argonne Nationwide Laboratory.

Kim Branson, senior vice chairman at GlaxoSmithKline, mentioned in a observation that the corporate has higher the complexity of the encoder fashions it generates whilst reducing coaching time by way of 80 instances. At Argonne, the chip is getting used for most cancers analysis and has decreased the experiment turnaround time on most cancers fashions by way of greater than 300 instances.

“For drug discovery, we’ve got different wins that we’ll be pronouncing over the following 12 months in heavy production and pharma and biotech and armed forces,” Feldman mentioned.

The brand new chips will send within the 0.33 quarter. Feldman mentioned the corporate now has greater than 300 engineers, with workplaces in Silicon Valley, Toronto, San Diego, and Tokyo.