Sébastien Taylor, Vision Technology Architect at Au-Zone Technologies, presents the “Tools and Techniques for Optimizing DNNs on Arm-based Processors with Au-Zone’s DeepView ML Toolkit” tutorial at the May 2019 Embedded Vision Summit.
In this presentation, Taylor describes methods and tools for developing, profiling and optimizing neural network solutions for deployment on Arm MCUs, CPUs and GPUs using Au-Zone’s DeepView ML Toolkit. He introduces the need for optimization to enable efficient deployment of deep learning models, and highlights the specific challenges of profiling and optimizing models for deployment in cost- and energy-constrained systems.
Taylor shows how Au-Zone’s DeepView tools can be used in conjunction with Arm’s Streamline tools to gain detailed insights into the performance of neural networks on ARM-based SoCs. Using a facial recognition solution as an example, he explores how to evaluate, profile and optimize deep learning models on a Cortex-M7 MCU, a Cortex-A73/A53 big.LITTLE CPU and a MALI G-71 GPU.