Cerebras Systems introduced its Wafer Scale Engine 2 (WSE-2) AI processor, boasting 2.6 trillion transistors and 850,000 AI optimized cores.
The wafer-sized processor, which is manufactured by TSMC on its 7nm-node, more than doubles all performance characteristics on the chip - the transistor count, core count, memory, memory bandwidth and fabric bandwidth - over the first generation WSE.
The processors powers the Cerebras CS-2 system, which the company says delivers hundreds or thousands of times more performance than legacy alternatives, replacing clusters of hundreds or thousands of graphics processing units (GPUs) that consume dozens of racks, use hundreds of kilowatts of power, and take months to configure and program. The CS-2 fits in one-third of a standard data center rack.
Early deployment sites for the first generation Cerebras WSE and CS-1 included Argonne National Laboratory, Lawrence Livermore National Laboratory, Pittsburgh Supercomputing Center (PSC) for its groundbreaking Neocortex AI supercomputer, EPCC, the supercomputing centre at the University of Edinburgh, pharmaceutical leader GlaxoSmithKline, and Tokyo Electron Devices, amongst others.
“At GSK we are applying machine learning to make better predictions in drug discovery, so we are amassing data – faster than ever before – to help better understand disease and increase success rates,” said Kim Branson, SVP, AI/ML, GlaxoSmithKline. “Last year we generated more data in three months than in our entire 300-year history. With the Cerebras CS-1, we have been able to increase the complexity of the encoder models that we can generate, while decreasing their training time by 80x. We eagerly await the delivery of the CS-2 with its improved capabilities so we can further accelerate our AI efforts and, ultimately, help more patients.”
“As an early customer of Cerebras solutions, we have experienced performance gains that have greatly accelerated our scientific and medical AI research,” said Rick Stevens, Argonne National Laboratory Associate Laboratory Director for Computing, Environment and Life Sciences. “The CS-1 allowed us to reduce the experiment turnaround time on our cancer prediction models by 300x over initial estimates, ultimately enabling us to explore questions that previously would have taken years, in mere months. We look forward to seeing what the CS-2 will be able to do with more than double that performance.”
