Scaling AI at Cerebras
Long training times are the single biggest factor slowing down innovation in deep learning. Today’s common approach of scaling large workloads out over many small processors is inefficient and requires extensive model tuning. With increasing model and dataset sizes, new ideas are needed to reduce training times.
Urs Köster explores trends in the computer vision and natural language processing domains and techniques for scaling with the Cerebras wafer scale engine—the largest chip in the world. Cerebras’s unique, purpose-built processor allows you to leverage sparsity for building larger models and enables model-parallel training as an efficient alternative to data-parallel training.
What you'll learn
- Discover new ideas for reducing training times
Urs Köster is the head of machine learning at Cerebras Systems, where he develops novel deep learning algorithms to enable the next generation of AI. He has 15 years of experience in neural networks and computational neuroscience, contributed to machine learning frameworks, developed low-precision numerical formats, and led data science engagements. Previously, he was head of algorithms R&D at Intel Nervana and a researcher at UC Berkeley.
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