DASS: Differentiable Architecture Search for Sparse neural networks

• URL: http://arxiv.org/abs/2207.06968v5
• Date: Tue, 12 Sep 2023 12:14:56 GMT
• Title: DASS: Differentiable Architecture Search for Sparse neural networks
• Authors: Hamid Mousavi, Mohammad Loni, Mina Alibeigi, Masoud Daneshtalab
• Abstract summary: We find that the architectures designed for dense networks by differentiable architecture search methods are ineffective when pruning mechanisms are applied to them. In this paper, we propose a new method to search for sparsity-friendly neural architectures. We do this by adding two new sparse operations to the search space and modifying the search objective.
• Score: 0.5735035463793009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the substantial gap between performance requirements and available processing power. While recent research has made significant strides in developing pruning methods to build a sparse network for reducing the computing overhead of DNNs, there remains considerable accuracy loss, especially at high pruning ratios. We find that the architectures designed for dense networks by differentiable architecture search methods are ineffective when pruning mechanisms are applied to them. The main reason is that the current method does not support sparse architectures in their search space and uses a search objective that is made for dense networks and does not pay any attention to sparsity. In this paper, we propose a new method to search for sparsity-friendly neural architectures. We do this by adding two new sparse operations to the search space and modifying the search objective. We propose two novel parametric SparseConv and SparseLinear operations in order to expand the search space to include sparse operations. In particular, these operations make a flexible search space due to using sparse parametric versions of linear and convolution operations. The proposed search objective lets us train the architecture based on the sparsity of the search space operations. Quantitative analyses demonstrate that our search architectures outperform those used in the stateof-the-art sparse networks on the CIFAR-10 and ImageNet datasets. In terms of performance and hardware effectiveness, DASS increases the accuracy of the sparse version of MobileNet-v2 from 73.44% to 81.35% (+7.91% improvement) with 3.87x faster inference time.

Related papers

• Flexible Channel Dimensions for Differentiable Architecture Search [50.33956216274694]
  We propose a novel differentiable neural architecture search method with an efficient dynamic channel allocation algorithm. We show that the proposed framework is able to find DNN architectures that are equivalent to previous methods in task accuracy and inference latency.
  arXiv  Detail & Related papers  (2023-06-13T15:21:38Z)
• Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization [50.50023451369742]
  Pruning-as-Search (PaS) is an end-to-end channel pruning method to search out desired sub-network automatically and efficiently. Our proposed architecture outperforms prior arts by around $1.0%$ top-1 accuracy on ImageNet-1000 classification task.
  arXiv  Detail & Related papers  (2022-06-02T17:58:54Z)
• D-DARTS: Distributed Differentiable Architecture Search [75.12821786565318]
  Differentiable ARchiTecture Search (DARTS) is one of the most trending Neural Architecture Search (NAS) methods. We propose D-DARTS, a novel solution that addresses this problem by nesting several neural networks at cell-level.
  arXiv  Detail & Related papers  (2021-08-20T09:07:01Z)
• Combined Depth Space based Architecture Search For Person Re-identification [70.86236888223569]
  We aim to design a lightweight and suitable network for person re-identification (ReID) We propose a novel search space called Combined Depth Space (CDS), based on which we search for an efficient network architecture, which we call CDNet. We then propose a low-cost search strategy named the Top-k Sample Search strategy to make full use of the search space and avoid trapping in local optimal result.
  arXiv  Detail & Related papers  (2021-04-09T02:40:01Z)
• Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection [14.792884010821762]
  This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems. DARTS operates upon a continuous, differentiable search space which enables both the architecture and parameters to be optimised via gradient descent.
  arXiv  Detail & Related papers  (2021-04-07T13:53:20Z)
• Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search [84.4140192638394]
  Most differentiable neural architecture search methods construct a super-net for search and derive a target-net as its sub-graph for evaluation. In this paper, we introduce EnTranNAS that is composed of Engine-cells and Transit-cells. Our method also spares much memory and computation cost, which speeds up the search process.
  arXiv  Detail & Related papers  (2021-01-27T12:16:47Z)
• ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding [86.40042104698792]
  We formulate neural architecture search as a sparse coding problem. In experiments, our two-stage method on CIFAR-10 requires only 0.05 GPU-day for search. Our one-stage method produces state-of-the-art performances on both CIFAR-10 and ImageNet at the cost of only evaluation time.
  arXiv  Detail & Related papers  (2020-10-13T04:34:24Z)
• Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation [4.595675084986132]
  The search space of neural architecture search (NAS) for convolutional neural network (CNN) is huge. We propose MoARR algorithm, which utilizes the existing research results and historical information to quickly find architectures that are both lightweight and accurate. Experimental results show that our MoARR can achieve a powerful and lightweight model (with 1.9% error rate and 2.3M parameters) on CIFAR-10 in 6 GPU hours.
  arXiv  Detail & Related papers  (2020-07-06T13:42:33Z)
• Fine-Grained Stochastic Architecture Search [6.277767522867666]
  Fine-Grained Architecture Search (FiGS) is a differentiable search method that searches over a much larger set of candidate architectures. FiGS simultaneously selects and modifies operators in the search space by applying a structured sparse regularization penalty. We show results across 3 existing search spaces, matching or outperforming the original search algorithms.
  arXiv  Detail & Related papers  (2020-06-17T01:04:14Z)
• AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks [15.740179244963116]
  Generative Adversarial Networks (GANs) are formulated as minimax game problems, whereby generators attempt to approach real data distributions by virtue of adversarial learning against discriminators. In this work, we aim to boost model learning from the perspective of network architectures, by incorporating recent progress on automated architecture search into GANs. We propose a fully differentiable search framework for generative adversarial networks, dubbed alphaGAN.
  arXiv  Detail & Related papers  (2020-06-16T13:27:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.