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Are Existing Knowledge Transfer Techniques Effective to Train Deep Networks On Edge Devices?

With the emergence of the edge computing paradigm, many edge applications, such as image recognition and augmented reality requires performing machine learning and artificial intelligence workloads on edge devices. Most ML models are large and computationally heavy, whereas edge devices are usually equipped with limited power and energy. Unfortunately, small models cannot perform well. Recent