AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design

18 Mar 2019Alexander WongZhong Qiu LinBrendan Chwyl

While deep neural networks have achieved state-of-the-art performance across a large number of complex tasks, it remains a big challenge to deploy such networks for practical, on-device edge scenarios such as on mobile devices, consumer devices, drones, and vehicles. In this study, we take a deeper exploration into a human-machine collaborative design approach for creating highly efficient deep neural networks through a synergy between principled network design prototyping and machine-driven design exploration... (read more)

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