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Pierre Paulin, Director of R&D for Embedded Vision at Synopsys, presents the “Trends in Neural Network Topologies for Vision at the Edge” tutorial at the September 2020 Embedded Vision Summit.

The widespread adoption of deep neural networks (DNNs) in embedded vision applications has increased the importance of creating DNN topologies that maximize accuracy while minimizing computation and memory requirements. This has led to accelerated innovation in DNN topologies.

In this talk, Paulin summarizes the key trends in neural network topologies for embedded vision applications, highlighting techniques employed by widely used networks such as EfficientNet and MobileNet to boost both accuracy and efficiency. He also touches on other optimization methods—such as pruning, compression and layer fusion—that developers can use to further reduce the memory and computation demands of modern DNNs.

See here for a PDF of the slides.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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