Efficient deep learning computing requires algorithm and hardware co-design to enable specialization. However, the extra degree of freedom creates a much larger design space. We propose AutoML techniques to architect efficient neural networks. We investigate automatically designing small and fast models (ProxylessNAS), auto channel pruning (AMC), and auto mixed-precision quantization (HAQ). We demonstrate such learning-based, automated design achieves superior performance and efficiency than rule-based human design. Moreover, we shorten the design cycle by 200× than previous work to efficiently search efficient models, so that we can afford to design specialized neural network models for different hardware platforms. We accelerate computation-intensive AI applications including (TSM) for efficient video recognition and PVCNN for efficient 3D recognition on point clouds. Finally, we’ll describe scalable distributed training and the potential security issues of efficient deep learning