Artificial intelligence and machine learning carry the promise of delivering optimization and personalization to all manner of systems. 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.
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At sufficient scale, the computational complexity of machine learning model training overwhelms general-purpose CPUs. 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.
The company announced that it raised an additional $150 million in fresh investor capital in an extension of its Series D round. The extension was led by Mayfair Equity Partners; new investors Baillie Gifford and M&G Investments joined in as well. The deal also saw participation from a number of prior investors. The first tranche of the company’s Series D was closed in December 2018, netting the company $200 million.
The Series D extension values Graphcore at $1.95 billion, according to the company. Taken together, the company has raised $460 million, according to Crunchbase data. The company’s shareholders include the likes of Draper Espirit, Dell Technologies Capital, C4 Ventures, various entities associated with Samsung, BMW i Ventures, Sequoia Capital and Microsoft, among others.
In a press release provided to Crunchbase News by the company, Graphcore highlighted a number of milestones from 2019. In partnership with strategic investor Dell Technologies, the companies co-developed and launched the DSS8440, a production-ready server built around Graphcore’s IPUs. Alongside Microsoft, another strategic investor, the company launched the Microsoft Azure IPU-Cloud service, as well as the IPU-Bare Metal Cloud service it launched in partnership with Cirrascale. The company’s publicly announced customers include Microsoft, Citadel Securities, Carmot Capital, and European search engine company Qwant.
The company says the new round brings its cash reserves up to $300 million. Graphcore has plotted for itself an ambitious growth plan. According to its press release, the company has devoted significant resources to research and development efforts. The company doubled headcount in its Bristol headquarters, as well as its engineering center in Oslo, Norway. It says its sales and support office in Palo Alto, California also saw similar up-scaling in 2019. The company also opened a new sales, support and engineering office in Beijing, alongside an engineering center in Cambridge, U.K., and an operations facility in Taiwan.
“Demand for our Intelligence Processor Unit products is increasing at existing and new customers and the outlook for our business in Fiscal 2020 is extremely positive. The major investments that we have made during 2018 and 2019 will help us to meet this strong demand by extending the capabilities of our technology and ecosystem, and will support long-term revenue growth and returns for our investors,” said Graphcore CEO Nigel Toon in a statement.
The company declined to answer questions from Crunchbase News about its revenue and profitability, whether it has its own fabrication facilities, what the company’s future exit prospects might look like and whether it may be affected by Brexit or the emerging SARS-CoV-2 virus situation in Asia.
Illustration: Li-Anne Dias
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