  Modern deep neural networks (DNNs) consist of millions of parameters,
necessitating high-performance computing during training and inference. Pruning
is one solution that significantly reduces the space and time complexities of
DNNs. Traditional pruning methods that are applied post-training focus on
streamlining inference, but there are recent efforts to leverage sparsity early
on by pruning before training. Pruning methods, such as iterative
magnitude-based pruning (IMP) achieve up to a 90% parameter reduction while
retaining accuracy comparable to the original model. However, this leads to
impractical runtime as it relies on multiple train-prune-reset cycles to
identify and eliminate redundant parameters. In contrast, training agnostic
early pruning methods, such as SNIP and SynFlow offer fast pruning but fall
short of the accuracy achieved by IMP at high sparsities. To bridge this gap,
we present Dual Gradient-Based Rapid Iterative Pruning (DRIVE), which leverages
dense training for initial epochs to counteract the randomness inherent at the
initialization. Subsequently, it employs a unique dual gradient-based metric
for parameter ranking. It has been experimentally demonstrated for VGG and
ResNet architectures on CIFAR-10/100 and Tiny ImageNet, and ResNet on ImageNet
that DRIVE consistently has superior performance over other training-agnostic
early pruning methods in accuracy. Notably, DRIVE is 43$\times$ to 869$\times$
faster than IMP for pruning.
