Advanced Computing in the Age of AI | Friday, March 29, 2024

Cerebras Systems Unveils the Industry’s First Trillion Transistor Chip 

LOS ALTOS, Calif., August 19, 2019 – Cerebras Systems, a startup dedicated to accelerating Artificial intelligence (AI) compute, today unveiled the largest chip ever built. Optimized for AI work, the Cerebras Wafer Scale Engine (WSE) is a single chip that contains more than 1.2 trillion transistors and is 46,225 square millimeters. The WSE is 56.7 times larger than the largest graphics processing unit which measures 815 square millimeters and 21.1 billion transistors1. The WSE also contains 3,000 times more high speed, on-chip memory, and has 10,000 times more memory bandwidth.

In AI, chip size is profoundly important. Big chips process information more quickly, producing answers in less time. Reducing the time-to-insight, or “training time,” allows researchers to test more ideas, use more data, and solve new problems. Google, Facebook, OpenAI, Tencent, Baidu, and many others argue that the fundamental limitation to today’s AI is that it takes too long to train models. Reducing training time removes a major bottleneck to industry-wide progress.

“Designed from the ground up for AI work, the Cerebras WSE contains fundamental innovations that advance the state-of-the-art by solving decades-old technical challenges that limited chip size—such as cross-reticle connectivity, yield, power delivery, and packaging,” said Andrew Feldman, founder and CEO of Cerebras Systems. “Every architectural decision was made to optimize performance for AI work. The result is that the Cerebras WSE delivers, depending on workload, hundreds or thousands of times the performance of existing solutions at a tiny fraction of the power draw and space.”

These performance gains are accomplished by accelerating all the elements of neural network training. A neural network is a multistage computational feedback loop. The faster inputs move through the loop, the faster the loop learns or “trains.” The way to move inputs through the loop faster is to accelerate the calculation and comand a low latency high bandwidth fabric together create the optimal architecture for accelerating AI work.

“While AI is used in a general sense, no two data sets or AI tasks are the same. New AI workloads continue to emerge and the data sets continue to grow larger,” said Jim McGregor, principal analyst and founder at TIRIAS Research. “As AI has evolved, so too have the silicon and platform solutions. The Cerebras WSE is an amazing engineering achievement in semiconductor and platform design that offers the compute, high-performance memory, and bandwidth of a supercomputer in a single wafer-scale solution.”

The Cerebras WSE’s record-breaking achievements could not have been made possible without years of close collaboration with TSMC, the world’s largest semiconductor foundry and leader in advanced process technologies. The WSE is manufactured by TSMC on its advanced 16nm process technology.

“We are very pleased with the result of our collaboration with Cerebras Systems in manufacturing the Cerebras Wafer Scale Engine, an industry milestone for wafer scale development,” said JK Wang, TSMC Senior Vice President of Operations. “TSMC’s manufacturing excellence and rigorous attention to quality enables us to meet the stringent defect density requirements to support the unprecedented die size of Cerebras’s innovative design.”

About Cerebras Systems
Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, and engineers of all types. We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art. The first announced element of the Cerebras solution is the Wafer Scale Engine (WSE). The WSE is the largest chip ever built. It contains 1.2 trillion transistors and covers more than 46,225 square millimeters of silicon. The largest graphics processor on the market has 21.1 billion transistors and covers 815 square millimeters. In artificial intelligence work, large chips process information more quickly producing answers in less time. As a result, neural networks that in the past took months to train, can train in minutes on the Cerebras WSE.


Source: Cerebras Systems 

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