Vivienne Sze, Associate Professor at MIT, presents the “Approaches for Energy Efficient Implementation of Deep Neural Networks” tutorial at the May 2018 Embedded Vision Summit.
Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks. But these algorithms are very computationally demanding. To enable DNNs to be used in practical applications, it’s critical to find efficient ways to implement them.
This talk explores how DNNs are being mapped onto today’s processor architectures, and how these algorithms are evolving to enable improved efficiency. Sze explores the energy consumption of commonly used CNNs versus their accuracy, and provides insights on “energy-aware” pruning of these networks.
