Graphcore's IPU technology uses "graph" processing, which processes all the data mapped across a single graph at once. It claims the IPU structure processes machine-learning tasks more efficiently than CPUs and GPUs. Many machine-learning frameworks -- including TensorFlow, MXNet, and Caffe -- already support graph processing.
Graphcore claims the vector processing model used by GPUs is "far more restrictive" than the graph model, which can allow researchers to "explore new models or reexplore areas" in AI research.
“Our Colossus IPUs are unique in having support for Stochastic Rounding on the arithmetic that is supported in hardware and runs at the full speed of the processor,” said Nigel Toon, CEO of Graphcore. “This allows the Colossus Mk2 IPU to keep all arithmetic in 16-bit formats, reducing memory requirements, saving on read and write energy and reducing energy in the arithmetic logic, while delivering full accuracy Machine Intelligence results.”
Customers who need more performance can also order IPU-POD64 systems powered by 16 IPU-M2000 machines (and therefore providing 16 PetaFLOPS), whereas large organizations can scale out to 64,000 IPUs for 16 ExaFLOPS at FP16.16/FP16.SR.