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takyon writes:

The five technical challenges Cerebras overcame in building the first trillion transistor chip

Superlatives abound at Cerebras, the until-today stealthy next-generation silicon chip company looking to make training a deep learning model as quick as buying toothpaste from Amazon. Launching after almost three years of quiet development, Cerebras introduced its new chip today — and it is a doozy. The "Wafer Scale Engine" is 1.2 trillion transistors (the most ever), 46,225 square millimeters (the largest ever), and includes 18 gigabytes of on-chip memory (the most of any chip on the market today) and 400,000 processing cores (guess the superlative).

It's made a big splash here at Stanford University at the Hot Chips conference, one of the silicon industry's big confabs for product introductions and roadmaps, with various levels of oohs and aahs among attendees. You can read more about the chip from Tiernan Ray at Fortune and read the white paper from Cerebras itself.

Also at BBC, VentureBeat, and PCWorld.

Original Submission

takyon writes:

Cerebras Unveils First Installation of Its AI Supercomputer at Argonne National Labs

At Supercomputing 2019 in Denver, Colo., Cerebras Systems unveiled the computer powered by the world's biggest chip. Cerebras says the computer, the CS-1, has the equivalent machine learning capabilities of hundreds of racks worth of GPU-based computers consuming hundreds of kilowatts, but it takes up only one-third of a standard rack and consumes about 17 kW. Argonne National Labs, future home of what's expected to be the United States' first exascale supercomputer, says it has already deployed a CS-1. Argonne is one of two announced U.S. National Laboratories customers for Cerebras, the other being Lawrence Livermore National Laboratory.

The system "is the fastest AI computer," says CEO and cofounder Andrew Feldman. He compared it with Google's TPU clusters (the 2nd of three generations of that company's AI computers), noting that one of those "takes 10 racks and over 100 kilowatts to deliver a third of the performance of a single [CS-1] box."

The CS-1 is designed to speed the training of novel and large neural networks, a process that can take weeks or longer. Powered by a 400,000-core, 1-trillion-transistor wafer-scale processor chip, the CS-1 should collapse that task to minutes or even seconds. However, Cerebras did not provide data showing this performance in terms of standard AI benchmarks such as the new MLPerf standards. Instead it has been wooing potential customers by having them train their own neural network models on machines at Cerebras.

[...] The CS-1's first application is in predicting cancer drug response as part of a U.S. Department of Energy and National Cancer Institute collaboration. It is also being used to help understand the behavior of colliding black holes and the gravitational waves they produce. A previous instance of that problem required 1024 out of 4392 nodes of the Theta supercomputer.

Also at TechCrunch, VentureBeat, and Wccftech.

Previously: Cerebras "Wafer Scale Engine" Has 1.2 Trillion Transistors, 400,000 Cores

Original Submission

takyon writes:

342 Transistors for Every Person In the World: Cerebras 2nd Gen Wafer Scale Engine Teased

One of the highlights of Hot Chips from 2019 was the startup Cerebras showcasing its product – a large 'wafer-scale' AI chip that was literally the size of a wafer. The chip itself was rectangular, but it was cut from a single wafer, and contained 400,000 cores, 1.2 trillion transistors, 46225 mm2 of silicon, and was built on TSMC's 16 nm process.

[...] Obviously when doing wafer scale, you can't just add more die area, so the only way is to optimize die area per core and take advantage of smaller process nodes. That means for TSMC 7nm, there are now 850,000 cores and 2.6 trillion transistors. Cerebras has had to develop new technologies to deal with multi-reticle designs, but they succeeded with the first gen, and transferred the learnings to the new chip. We're expecting more details about this new product later this year.

Previously: Cerebras "Wafer Scale Engine" Has 1.2 Trillion Transistors, 400,000 Cores
Cerebras Systems' Wafer Scale Engine Deployed at Argonne National Labs

Original Submission

upstart writes in with an IRC submission:

The Trillion-Transistor Chip That Just Left a Supercomputer in the Dust:

So, in a recent trial, researchers pitted the chip—which is housed in an all-in-one system about the size of a dorm room mini-fridge called the CS-1—against a supercomputer in a fluid dynamics simulation. Simulating the movement of fluids is a common supercomputer application useful for solving complex problems like weather forecasting and airplane wing design.

The trial was described in a preprint paper written by a team led by Cerebras's Michael James and NETL's Dirk Van Essendelft and presented at the supercomputing conference SC20 this week. The team said the CS-1 completed a simulation of combustion in a power plant roughly 200 times faster than it took the Joule 2.0 supercomputer to do a similar task.

The CS-1 was actually faster-than-real-time. As Cerebrus wrote in a blog post, "It can tell you what is going to happen in the future faster than the laws of physics produce the same result."

The researchers said the CS-1's performance couldn't be matched by any number of CPUs and GPUs. And CEO and cofounder Andrew Feldman told VentureBeat that would be true "no matter how large the supercomputer is." At a point, scaling a supercomputer like Joule no longer produces better results in this kind of problem. That's why Joule's simulation speed peaked at 16,384 cores, a fraction of its total 86,400 cores.

Previously:
Cerebras More than Doubles Core and Transistor Count with 2nd-Generation Wafer Scale Engine
Cerebras Systems' Wafer Scale Engine Deployed at Argonne National Labs
Cerebras "Wafer Scale Engine" Has 1.2 Trillion Transistors, 400,000 Cores

Original Submission