Cerebras’ wafer-size chip is 10,000 times faster than a GPU

Cerebras Techniques and the federal Division of Power’s Nationwide Power Generation Laboratory nowadays introduced that the corporate’s CS-1 gadget is greater than 10,000 instances sooner than a graphics processing unit (GPU).

On a realistic stage, this implies AI neural networks that in the past took months to coach can now educate in mins at the Cerebras gadget.

Cerebras makes the arena’s greatest laptop chip, the WSE. Chipmakers in most cases slice a wafer from a 12-inch-diameter ingot of silicon to procedure in a chip manufacturing unit. 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 an advanced option to different cores. The interconnections are designed to stay the entire cores performing at top speeds so the transistors can paintings in combination as one.

Cerebras’s CS-1 gadget makes use of the WSE wafer-size chip, which has 1.2 trillion transistors, the fundamental on-off digital switches which are the construction blocks of silicon chips. Intel’s first 4004 processor in 1971 had 2,300 transistors, and the Nvidia A100 80GB chip, introduced the previous day, has 54 billion transistors.

Feldman mentioned in an interview with VentureBeat that the CS-1 was once additionally 200 instances sooner than the Joule Supercomputer, which is No. 82 on a listing of the highest 500 supercomputers on the earth.

“It displays record-shattering efficiency,” Feldman mentioned. “It additionally displays that wafer scale generation has packages past AI.”

Above: The Cerebras WSE has 1.2 trillion transistors in comparison to Nvidia’s greatest GPU, the A100 at 54.2 billion transistors.

Those are culmination of the novel method Los Altos, California-based Cerebras has taken, making a silicon wafer with 400,000 AI cores on it as a substitute of chopping that wafer into person chips. The bizarre design makes it so much more uncomplicated to perform duties since the processor and reminiscence are nearer to one another and feature numerous bandwidth to attach them, Feldman mentioned. The query of ways extensively acceptable the method is to other computing duties stays.

A paper in keeping with the result of Cerebras’ paintings with the federal lab mentioned the CS-1 can ship efficiency this is unimaginable with any selection of central processing devices (CPUs) and GPUs, that are each recurrently utilized in supercomputers. (Nvidia’s GPUs are utilized in 70% of the highest supercomputers now). Feldman added that that is true “regardless of how huge that supercomputer is.”

Cerebras is presenting on the SC20 supercomputing on-line tournament this week. The CS-1 beat the Joule Supercomputer at a workload for computational fluid dynamics, which simulates the motion of fluids in puts similar to a carburetor. The Joule Supercomputer prices tens of tens of millions of greenbacks to construct, with 84,000 CPU cores unfold over dozens of racks, and it consumes 450 kilowatts of energy.


Above: Cerebras has a half-dozen or so supercomputing consumers.

Symbol Credit score: LLNL

On this demo, the Joule Supercomputer used 16,384 cores, and the Cerebras laptop was once 200 instances sooner, in keeping with power lab director Brian Anderson. Cerebras prices a number of million greenbacks and makes use of 20 kilowatts of energy.

“For those workloads, the wafer-scale CS-1 is the quickest gadget ever constructed,” Feldman mentioned. “And it’s sooner than some other aggregate or cluster of alternative processors.”

A unmarried Cerebras CS-1 is 26 inches tall, suits in one-third of a rack, and is powered by way of the trade’s most effective wafer-scale processing engine, Cerebras’ WSE. It combines reminiscence efficiency with huge bandwidth, low latency interprocessor verbal exchange, and an structure optimized for prime bandwidth computing.

The analysis was once led by way of Dirk Van Essendelft, gadget studying and knowledge science engineer at NETL, and Michael James, Cerebras cofounder and leader architect of complex applied sciences. The effects got here after months of labor.

In September 2019, the Division of Power introduced its partnership with Cerebras, together with deployments with Argonne Nationwide Laboratory and Lawrence Livermore Nationwide Laboratory.

The Cerebras CS-1 was once introduced in November 2019. The CS-1 is constructed across the WSE, which is 56 instances greater, has 54 instances extra cores, 450 instances extra on-chip reminiscence, five,788 instances extra reminiscence bandwidth, and 20,833 instances extra cloth bandwidth than the main GPU competitor, Cerebras mentioned.


Above: Cerebras on the Lawrence Livermore Nationwide Lab.

Symbol Credit score: LLNL

Relying on workload, from AI to HPC, the CS-1 delivers loads or 1000’s of instances extra compute than legacy possible choices, and it does so at a fragment of the ability draw and house.

Feldman famous that the CS-1 can end calculations sooner than actual time, that means it could possibly get started the simulation of an influence plant’s response core when the response begins and end the simulation ahead of the response ends.

“Those dynamic modeling issues have a fascinating feature,” Feldman mentioned. “They scale poorly throughout CPU and GPU cores. Within the language of the computational scientist, they don’t showcase ‘sturdy scaling.’ Because of this past a undeniable level, including extra processors to a supercomputer does now not yield further efficiency positive aspects.”

Cerebras has raised $450 million and has 275 workers.

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