“How Transformers are Changing the Direction of Deep Learning Architectures,” a Presentation from Synopsys

Tom Michiels, System Architect for DesignWare ARC Processors at Synopsys, presents the “How Transformers are Changing the Direction of Deep Learning Architectures” tutorial at the May 2022 Embedded Vision Summit.

The neural network architectures used in embedded real-time applications are evolving quickly. Transformers are a leading deep learning approach for natural language processing and other time-dependent, series data applications. Now, transformer-based deep learning network architectures are also being applied to vision applications with state-of-the-art results compared to CNN-based solutions.

In this presentation, Michiels introduces transformers and contrasts them with the CNNs commonly used for vision tasks today. He examines the key features of transformer model architectures and shows performance comparisons between transformers and CNNs. He concludes the presentation with insights on why Synopsys thinks transformers are an important approach for future visual perception tasks.

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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