Machine learning (ML) technology is being increasingly adopted across the embedded sector in applications ranging from consumer to industrial IoT. All because of its unique potential for innovation. With the combination of popular neural network frameworks such as Caffe and TensorFlow, embedded-optimized neural network software stack for Cortex-M processors, and high performance devices such as the NXP i.MX RT crossover processor series, exciting new possibilities are opened for ML applications at the end node. This means that a wide range of neural network applications, like image and audio recognition, can now be applied to Cortex-M based processors with optimized performance and energy efficiency.
In this joint Arm and NXP webinar, you’ll learn how Arm NN and CMSIS-NN can help you develop efficient neural network applications for Cortex-M devices. Using a practical example, we will show how the powerful i.MX RT processors can be used in conjunction with CMSIS-NN to run applications like keyword spotting. You will also learn how the on-chip Floating Point Unit (FPU) accelerates feature extraction from a live audio stream.
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