A central problem in the deployment of deep neural networks is maximizing accuracy within the compute performance constraints of low power devices. In this talk, Peter Vajda, Research Manager, Facebook, discusses approaches to addressing this challenge based automated network search and adaptation algorithms. These algorithms not only discover neural network models that surpass state-of-the-art accuracy, but are also able to adapt models to achieve efficient implementation on low power platforms for real-world applications.
Hardware-Aware Deep Neural Architecture Search
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