Spatial light modulator approaches for reducing scattered and out-of-focus light

Wang, Yao L.; Civale, Sabrina C.; Grooms, Noa W. F.; Chung, Samuel

doi:10.1117/12.2584545

Cited by 1 publication

(4 citation statements)

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“…To enable a comprehensive comparison with more SOTA frameworks, we employ ResNet-50 as the baseline network. In addition, we also compare our DR-ResNet50 [12] SSS-ResNet50 [51] Versatile [52] PFP-A [53] C-SGD70 [54] GAL-1 [55] RANet [42] DECORE [18] DBP [17] Taylor [16] AutoPruner [56] HRank [13] ResNets [43] RCC [11] WidthShrink [10] DepthShrink [43] EC-Static (ours) EC-Dynamic (ours) compressed models with many popular backbone architectures in different computation regimes.…”

Section: G Results On Imagenet-1kmentioning

confidence: 99%

“…Among channel pruning approaches, MobileNetV2 [8] and Slimmable networks [13] remove channels from all layers uniformly, while Taylor pruning [14], HRank [11], and DECORE [16] evaluate the global importance of each channel and then prune channels in a layer-wise manner. Both depth pruning and channel pruning focus on compressing the network architecture, while resolution pruning [8]- [10], [17] optimizes the spatial redundancy in input images by shrinking images to smaller resolutions or selectively cropping images for inference. MNasNet [9] reduces the spatial redundancy by utilizing a fixed small resolution for all images during inference.…”

Section: Related Workmentioning

confidence: 99%

“…Instead, DR-ResNet [10] introduces a dynamic resolution strategy to dynamically assign different resolutions to different images according to their recognition complexity. Moreover, GFNet [17] introduces a Glance-and-Focus inference strategy, which utilizes both the shrunk version and small local patches of an image for inference to accelerate CNNs while preserving high accuracy. However, all above methods only consider reducing the redundancy in a single dimension and thus only achieve a limited compression rate.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, given a resource budget, existing approaches usually yield a fixed compressed neural network and resolution for all images. However, as discussed in [10], [17], different images are of distinct recognition difficulties, using a static model and resolution to process all images can lead to inefficient utilization of computation, achieving only suboptimal efficiency and accuracy. Practically, for images with simple features, a small CNN model is adequate to generate correct results.…”

Section: Introductionmentioning

confidence: 99%

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EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI

Kong

Liu

Huai

et al. 2023

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment of CNNs onto resource-constrained embedded devices. To address this issue, we propose EdgeCompress, a comprehensive compression framework to reduce the computational overhead of CNNs. In EdgeCompress, we first introduce dynamic image cropping, where we design a lightweight foreground predictor to accurately crop the most informative foreground object of input images for inference, which avoids redundant computation on background regions. Subsequently, we present compound shrinking to collaboratively compress the three dimensions (depth, width, and resolution) of CNNs according to their contribution to accuracy and model computation. Dynamic image cropping and compound shrinking together constitute a multi-dimensional CNN compression framework, which is able to comprehensively reduce the computational redundancy in both input images and neural network architectures, thereby improving the inference efficiency of CNNs. Further, we present a dynamic inference framework to efficiently process input images with different recognition difficulties, where we cascade multiple models with different complexities from our compression framework and dynamically adopt different models for different input images, which further compresses the computational redundancy and improves the inference efficiency of CNNs, facilitating the deployment of advanced CNNs onto embedded hardware. Experiments on ImageNet-1K demonstrate that EdgeCompress reduces the computation of ResNet-50 by 48.8% while improving the top-1 accuracy by 0.8%. Meanwhile, we improve the accuracy by 4.1% with similar computation compared to HRank. the stateof-the-art compression framework. The source code and models are available at https://github.com/ntuliuteam/edge-compress

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