Hardware-aware Deep Neural Network Design

A central problem in the deployment of deep neural networks is maximizing accuracy within the compute performance constraints of embedded devices. In this talk, we will discuss approaches to addressing this challenge based on 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 diverse processing platforms for real-world applications.