Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers
This addresses the need for compact and efficient neural networks for deployment in resource-constrained environments, representing a novel method rather than an incremental improvement.
The paper tackles the problem of finding efficient sparse neural networks by introducing Dynamic Sparse Training, a novel pruning algorithm that jointly optimizes network parameters and sparse structure with trainable masks, achieving state-of-the-art performance with minimal performance loss using the same training epochs as dense models.
We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds. These thresholds can have fine-grained layer-wise adjustments dynamically via backpropagation. We demonstrate that our dynamic sparse training algorithm can easily train very sparse neural network models with little performance loss using the same number of training epochs as dense models. Dynamic Sparse Training achieves the state of the art performance compared with other sparse training algorithms on various network architectures. Additionally, we have several surprising observations that provide strong evidence for the effectiveness and efficiency of our algorithm. These observations reveal the underlying problems of traditional three-stage pruning algorithms and present the potential guidance provided by our algorithm to the design of more compact network architectures.