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Vikas Chandra, Senior Principal Engineer and Director of Machine Learning at Arm, presents the “Deep Learning on Arm Cortex-M Microcontrollers” tutorial at the May 2018 Embedded Vision Summit.

Deep learning algorithms are gaining popularity in IoT edge devices due to their human-level accuracy in many tasks, such as image classification and speech recognition. As a result, there is increasing interest in deploying neural networks (NNs) on the types of low-power processors found in always-on systems, such as those based on Arm Cortex-M microcontrollers.

In this talk, Chandra introduces the challenges of deploying neural networks on microcontrollers with limited memory and compute resources and power budgets. He introduces CMSIS-NN, a library of optimized software kernels to enable deployment of neural networks on Cortex-M cores. He also presents techniques for NN algorithm exploration to develop lightweight models suitable for resource constrained systems, using image classification as an example.

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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