Below are some points coming from the article in Financial Times:
His company’s bet is that over the next decade every high-tech industry, from automotive to health, security and manufacturing, will need to integrate machine learning into their systems, allowing computers to spot patterns and make discoveries from large data sets.
“This is only the third time in the history of computing that there is a need for new microprocessors,” he said. “The first was when we founded Arm, where low-power chips became powerful in mobile phones, the second time was GPUs, which were needed for high-intensity video processing and the third time is now. It’s very unusual.”
But when Google announced in 2016 that it was building its own AI-focused chips for internal applications, suddenly investors began to take notice.
Venture capitalists invested more than $1.5bn in chip start-ups in 2017, nearly twice the investments made in 2015, according to the research firm CB Insights. UBS has predicted that global AI chip revenue will grow to $35bn in 2021, nearly six times its value in 2016. Graphcore claims its advantage is that the data needed to train algorithms sit on its chips, rather than externally. This feature, and the chip’s communication network and huge array of processors, means that Graphcore systems are 10 to 100 times faster than existing chips for applications such as image recognition, voice processing and video analysis.
In December, Graphcore raised $200m in a new funding round from investors Microsoft and BMW as well as existing investors including Sequoia Capital and Amadeus Capital, valuing the company at $1.5bn.
Microsoft has AI across products such as Office 365 for email, Skype for real-time language translation, LinkedIn and Azure cloud services. Graphcore chips will be tested internally across some of these products, sources close to the partnership said.
Murgia, M. (2019). UK start-up Graphcore aims to dominate AI chip industry | Financial Times. [online] Ft.com. Available at: https://www.ft.com/content/1a0ed6c8-18ee-11e9-b93e-f4351a53f1c3 [Accessed 23 Jan. 2019].
