Specialized AI Chipmaker Graphcore Extends Series D Round With $150M, Valued At $1.95B

The challenge is that the math behind it is somewhat complicated, and that it has to be run, over and over, across vast quantities of data to suss out the statistical weights and biases of a particular system. The work will get done; it might just take a long time. Data scientists and machine learning researchers have long used graphics processing units (GPUs) because of their highly parallelized architecture and relatively abundant on-chip memory available. But as industry and research groups alike seek more efficiency and need to accommodate ever-larger quantities of information, more specialized computing hardware is required for the task. Headquartered in Bristol, U.K., Graphcore is in the business of producing silicon purpose-built for munching through machine-learning math at high rates of speed and using less electricity than GPUs. Benchmarks for Graphcore's Intelligence Processing Unit (IPU) state that it offers notably less latency and higher computational throughput, and uses less power than GPUs.