According to a recent press release, “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 transistors. 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.”
Andrew Feldman, founder and CEO of Cerebras Systems, stated, “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… 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.”
The release adds, “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 communication within the loop.”
Read more at Business Wire.
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