Machine Learning on large scale graphs presents several unique challenges, due to the sparsity of the connections. Exact computation is often intractable on current accelerators, and algorithmic approximations fall short of modelling interesting aspects like long range dependencies effectively.
We present how Graphcore’s IPU design tackles these challenges, creating the opportunity to accelerate deep GNNs on large graphs. This talk aims at stimulating Data Scientists, Machine Learning Researchers and Engineers to think about different ways to deploy current large scale GNNs and to develop algorithms that exploit the full potential of our new hardware architecture.
About This Speaker
Carlo Luschi 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 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. ---
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