We propose a data-driven approach for deep convolutional neural network compression that achieves high accuracy with high throughput and low memory requirements. Current network compression methods either find a low-rank factorization of the features that requires more memory, or select only a subset of features by pruning entire filter channels. We propose the Cascaded Projection (CaP) compression method that projects the output and input filter channels of successive layers to a unified low dimensional space based on a low-rank projection. We optimize the projection to minimize classification loss and the difference between the next layer’s features in the compressed and uncompressed networks. To solve this non-convex optimization problem we propose a new optimization method of a proxy matrix using back propagation and Stochastic Gradient Descent (SGD) with geometric constraints. Our cascaded projection approach leads to improvements in all critical areas of network compression: high accuracy, low memory consumption, low parameter count and high processing speed. The proposed CaP method demonstrates
state-of-the-art results compressing VGG16 and ResNet networks with over 4× reduction in the number of computations and excellent performance in top-5 accuracy on the ImageNet dataset before and after fine-tuning.
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DNR: A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs
This paper presents a dynamic network rewiring (DNR) method to generate pruned deep neural network (DNN) models that are robust against adversarial attacks yet maintain high accuracy on clean im- ages. In particular, the disclosed DNR method is based on a unified constrained optimization formulation using a hybrid loss function that merges ultra-high model compression with robust adversar- ial training. This training strategy dynamically adjusts inter-layer connectivity based on per-layer normalized momentum computed from the hybrid loss function. In contrast to existing robust pruning frameworks that require multiple training iterations, the proposed learning strategy achieves an overall target pruning ratio with only a single training iteration and can be tuned to support both irregu- lar and structured channel pruning. To evaluate the merits of DNR, experiments were performed with two widely accepted models, namely VGG16 and ResNet-18, on CIFAR-10, CIFAR-100 as well as with VGG16 on Tiny-ImageNet. Compared to the baseline un- compressed models, DNR provides over 20× compression on all the datasets with no significant drop in either clean or adversarial classification accuracy. Moreover, our experiments show that DNR consistently finds compressed models with better clean and adver- sarial image classification performance than what is achievable through state-of-the-art alternatives. Our models and test codes are available at https://github.com/ksouvik52/DNR_ASP_DAC2021.
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- Award ID(s):
- 1763747
- NSF-PAR ID:
- 10295993
- Date Published:
- Journal Name:
- 26th Asia and South Pacific Design Automation Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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