Dr. Natalia Vassilieva - Technical Product Manager - Cerebras Systems

Title: Accelerating Deep Learning with a purpose-built solution: the Cerebras approach

Abstract: The new era of chip specialization for deep learning is here. Traditional approaches to computing can no longer meet the computational and power requirements of this workload, arguably the most important of our generation. What is the right processor for deep learning? To answer this question, this talk will provide an overview of deep neural nets, discuss computational requirements of different types of models and limitations of existing hardware architectures and scale-out approaches. Then we will discuss Cerebras' approach to meet computational requirements of deep learning with the Cerebras Wafer Scale Engine (WSE) -- the largest computer chip in the world, and the Cerebras Software Platform, co-designed with the WSE. The WSE provides cluster-scale resources on a single chip with full utilization for tensors of any shape -- fat, square and thin, dense and sparse -- enabling researchers to explore novel network architectures and optimization techniques at any batch sizes. Finally, we will discuss potential co-design ideas for new neural net models and learning methods for the WSE.

Keywords: WSE, training, inference, dataflow computational graph

Bio: Natalia Vassilieva is a Technical Product Manager at Cerebras Systems, a computer systems company dedicated to accelerating deep learning. Her focus is machine learning and artificial intelligence, analytics, and application-driven software-hardware optimization and co-design. Most recently before joining Cerebras Natalia has been a Sr. Research Manager at Hewlett Packard Labs, where she led the Software and AI group and served as the head of HP Labs Russia from 2011 till 2015. Prior to HPE, she was an Associate Professor at St. Petersburg State University and worked as a software engineer for different IT companies. Natalia holds a PhD in computer science from St. Petersburg State University.