DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices

Understand

Deploying deep neural networks on mobile devices is a challenging task.

• Current model compression methods such as matrix decomposition effectively reduce the deployed model size, but still cannot satisfy real-time processing requirement.
• This paper first discovers that the major obstacle is the excessive execution time of non-tensor layers such as pooling and normalization without tensor-like trainable parameters.
• This motivates us to design a novel acceleration framework: DeepRebirth through "slimming" existing consecutive and parallel non-tensor and tensor layers.