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Found 7 papers, 4 papers with code
Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
Robustness and compactness are two essential attributes of deep learning models that are deployed in the real world.
Soft Masking for Cost-Constrained Channel Pruning
Structured channel pruning has been shown to significantly accelerate inference time for convolution neural networks (CNNs) on modern hardware, with a relatively minor loss of network accuracy.
Structural Pruning via Latency-Saliency Knapsack
We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing the accuracy while constraining latency under a predefined budget on targeting device.
When to Prune? A Policy towards Early Structural Pruning
Through extensive experiments on ImageNet, we show that EPI empowers a quick tracking of early training epochs suitable for pruning, offering same efficacy as an otherwise ``oracle'' grid-search that scans through epochs and requires orders of magnitude more compute.
HALP: Hardware-Aware Latency Pruning
We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing the accuracy while constraining latency under a predefined budget.
Global Vision Transformer Pruning with Hessian-Aware Saliency
This work aims on challenging the common design philosophy of the Vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage, where we redistribute the parameters both across transformer blocks and between different structures within the block via the first systematic attempt on global structural pruning.
Optimal Quantization Using Scaled Codebook
We study the problem of quantizing N sorted, scalar datapoints with a fixed codebook containing K entries that are allowed to be rescaled.
