Deep learning inference is a crucial phase in machine learning pipelines, enabling trained neural network models to provide valuable insights to end users. These models are typically deployed for predictive endeavors such as image classification, object detection, and semantic segmentation. Nevertheless, various constraints can hinder the implementation of inference at scale on edge devices like IoT controllers and gateways. In this blog, we will explore how to effectively train and convert a neural network model for image classification into an edge-optimized binary specifically for Intel FPGA hardware.
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