Graphcore Ltd, a startup developing new technology to deliver massive acceleration for machine learning and AI applications, has completed a $30m Series-A funding round from a world-class line up of venture capital and strategic investors.
The funding was led by Robert Bosch Venture Capital GmbH with Samsung Catalyst Fund and other major technology firms, alongside leading venture capital funds from London, Silicon Valley and Israel: Amadeus Capital Partners, C4 Ventures, Draper Esprit plc, Foundation Capital and Pitango Venture Capital.
Graphcore has spent the last two years building an experienced hardware and software team to develop a system designed from the ground up to accelerate both current and next generation machine intelligence applications such as natural language dialogue, autonomous vehicles and personalized medicines.
Graphcore CEO and co-founder, Nigel Toon, said, “Machine intelligence will have a bigger impact on our lives over the next 10 years than mobile technology has had in the last two decades. Next generation machine intelligence will allow us to translate foreign languages in real-time, help diagnose illnesses and develop personalized treatments, control robots that clean our houses and offices, drive cars autonomously and provide us with intelligent digital assistants that can help us organize our busy lives. The IPU is the first system specifically designed for machine intelligence.”
The company will bring its IPU (Intelligent Processing Unit) system to market in 2017 with the IPU-Appliance™ designed to lower the cost of accelerating AI applications in cloud and enterprise datacenters. The IPU-Appliance aims to increase the performance of both training and inference by between 10x and 100x compared to the fastest systems in use today.
The company also plans to make its low power IPU technology available for embedded consumer applications including autonomous cars, collaborative robots and intelligent mobile devices.
IPU systems will accelerate the full range of training, inference, and prediction approaches. Its huge computational resources and software tools and libraries are flexible and easy to use, allowing researchers to explore machine intelligence across a much broader front than the current focus on feed-forward neural networks. This technology will enable recent success in deep learning to evolve rapidly towards useful, general artificial intelligence.
A Bank of America Merrill Lynch report citing IDC research recently predicted that the AI industry will exceed $70 billion by 2020 and Tractica predicts that spending on hardware for deep learning projects will grow from $436 million in 2015 to $41.5 billion by 2024.