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The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. In the lead up to our 5th annual event on June 28th 2019 in London, we’re running a series of speaker profiles to shed more light on what you can expect to learn on the day!
A driving force behind the blossoming of machine intelligence is next-generation hardware that accelerates training and inference at scale. In 2017, we hosted Graphcore Co-Founder and CTO Simon Knowles. In his talk, Simon motivated the need for novel hardware and outlined the design principles behind the Company’s Intelligence Processing Unit (IPU). Fast forward two years, Graphcore has established itself as a World-leader in machine intelligence hardware and has raised a further $280M from the likes of Sequoia, Atomico, Sofina, Microsoft and BMW.
It's in this context that we’re extremely excited to welcome Carlo Luschi, Director of Research at Graphcore, to RAAIS 2019! Carlo is responsible for the study and development of algorithms for machine intelligence. Prior to Graphcore, Carlo was a Member of Technical Staff at Bell Labs Research, Lucent Technologies, and more recently Director of Algorithms and Standards at Icera Inc., which was acquired by NVIDIA in 2011. Carlo subsequently served as Director of Algorithms and Standards at NVIDIA until joining Graphcore in 2016. Carlo is also a prolific inventor, having authored 55 patents granted or pending. He holds a PhD in Electrical Engineering from the University of Edinburgh.
In his talk at RAAIS 2019, Carlo will focus on the ground-breaking architecture of Graphcore’s IPU. The processor is based on a revolutionary approach for the efficient use of massively parallel computing resources for Machine Intelligence applications, under the current physical constraints. Carlo will address some of the fundamental work carried out at Graphcore Research to support the realization of the above goal. This work is based on the adoption of an efficient numerical representation and through algorithmic innovation, including optimization approaches based on evolutionary computing and new solutions for the training of large-scale distributed machines.
You can read about Carlo’s views on the directions of AI research on Graphcore’s blog here.