Machine learning networks can require quite a few resources to train and execute. So how can this be done in an embedded environment? In this session, we will explore several system architectures that developers can use to achieve machine learning at the edge. We will also discuss why machine learning is being pushed from the cloud to the edge, and will examine software resources that are available on the Arm Cortex-M, such as CMSIS-NN.
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