DT-CNN: An Energy-Efficient Dilated and Transposed Convolutional Neural Network Processor for Region of Interest Based Image Segmentation

Im, Dongseok; Han, Donghyeon; Choi, Sungpill; Kang, Sanghoon; Yoo, Hoi‐Jun

doi:10.1109/tcsi.2020.2991189

Cited by 31 publications

(8 citation statements)

References 27 publications

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“…. In this study, we compare the proposed model to several others used for digital image segmentation, including CNN [19], recurrent neural network (RNN) [20], long short-term memory (LSTM) [21], Unet [22], and DenseNet [23]. Tables 1 and 2 display the models' segmentation results on the MIT liver tumor dataset.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

Xing,

Ku,

Zhao

2024

International Journal of Intelligent Systems

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To divide a digital image into individual parts that share similar characteristics is known as digital image segmentation, and it is a vital research subject in the field of computer vision. Object recognition, medical imaging, surveillance, and video processing are just a few of the many real-world contexts where this study could prove useful. While digital image segmentation research has come a long way, there are still certain obstacles to overcome. Segmentation algorithms frequently encounter challenges in achieving both accuracy and efficiency when confronted with intricate settings, noisy pictures, or fluctuating lighting conditions. The absence of established evaluation standards adds complexity to the process of performing equitable comparisons among different segmentation methodologies. Due to the subjective nature of photo segmentation, attaining consistent results among specialists can be challenging. The integration of machine learning and deep neural networks into segmentation algorithms has introduced new challenges, including the need for large amounts of annotated data and the interpretability of the outcomes. Given these challenges, the objective of this study is to enhance the segmentation model. To this end, this research suggests a model of convolutional neural networks that is optimal for digital picture segmentation. The model is based on a dense convolution neural network, and it incorporates a transfer learning technique to significantly boost the model’s robustness and the quality of picture segmentation. The model’s adaptability to new datasets is improved by the incorporation of a transfer learning method. As demonstrated by experimental results on two publicly available datasets, the suggested methodology considerably enhances the resilience of digital picture segmentation.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

Xing,

Ku,

Zhao

2024

International Journal of Intelligent Systems

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“…[22] similarly works on full lines and limits the fused layers to one residual block. The latter is also a limitation of [23]. [16] relies on recomputation of features in overlapped parts of ROI pyramids and thus requires a large enough output patch size to avoid too many recom-putations (see section 2).…”

Section: Cnnmentioning

confidence: 99%

DepFiN: A 12-nm Depth-First, High-Resolution CNN Processor for IO-Efficient Inference

Goetschalckx

Verhelst³

2023

IEEE J. Solid-State Circuits

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Applying Convolutional Neural Networks on high-resolution images leads to very large intermediate feature maps, which dominate the memory traffic. Processing in the classical layer-by-layer order creates the requirement to store the complete feature maps at once, when moving from one layer to the next. As the size of these feature maps only realistically allows this in off-chip memory, this leads to high off-chip bandwidth, which comes at great energy costs. The DepFiN processor chip, presented in this paper, overcomes this cost by running CNNs in a deep layer fusion mode, dubbed depth-first execution, made possible by a control flow that supports frequently switching between layers. To furthermore tackle the computational cost as well, the computationally efficient depth-wise+pointwise layer pairs are explicitly supported in DepFiN by a novel accelerator core that can dynamically change its configuration to manage the low computational intensity of the depthwise layers. Benchmarking measurements show the 12nm DepFiN chip reaching up to 20 TOPS/W peak, 8.2 TOPS/W on the MC-CNN-fast stereo-matching network excl. IO power (at 8-bit 0.6 Vdd), and, crucially, 3.95 TOPS/W with the IO power included on the same network and an up to 18× improvement realized by supporting depth-first (MC-CNN-fast at 8-bit, 0.65V Vdd).

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“…Numerous techniques, including transposed convolution, can be used as an up-sampling layer on a CNN. Transposed convolution enlarges the feature map by returning the convolution input value [51]. The steps of the transposed convolution process are to filled the position between each matrix entry with 0, where the number of positions is the step value minus 1 [52].…”

Section: ) Convolutional Transposedmentioning

confidence: 99%

VG-DropDNet a Robust Architecture for Blood Vessels Segmentation on Retinal Image

et al. 2022

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Additional layers to the U-Net architecture leads to additional parameters and network complexity. The Visual Geometry Group (VGG) architecture with 16 backbones can overcome the problem with small convolutions. Dense Connected (DenseNet) can be used to avoid excessive feature learning in VGG by directly connecting each layer using input from the previous feature map. Adding a Dropout layer can protect DenseNet from Overfitting problems. This study proposes a VG-DropDNet architecture that combines VGG, DenseNet, and U-Net with a dropout layer in blood vessels retinal segmentation. VG-DropDNet is applied to Digital Retina Image for Vessel Extraction (DRIVE) and Retina Structured Analysis (STARE) datasets. The results on DRIVE give great accuracy of 95.36%, sensitivity of 79.74% and specificity of 97.61%. The F1-score on DRIVE of 0.8144 indicates that VG-DropDNet has great precision and recall. The IoU result is 68.70. It concludes that the resulting image of VG-DropDNet has a great resemblance to its ground truth. The results on STARE are excellent for accuracy of 98.56%, sensitivity of 91.24%, specificity of 92.99% and IoU of 86.90%. The results of the VGG-DropDNet on STARE show that the proposed method is excellent and robust for blood vessels retinal segmentation. The Cohen's Kappa coefficient obtained by VG-DropDNet at DRIVe is 0.8386 and at STARE is 0.98, it explains that the VG-DropDNet results are consistent and precise in both datasets. The results on various datasets indicate that VG-DropDnet is effective, robust and stable in retinal image blood vessel segmentation.

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DT-CNN: An Energy-Efficient Dilated and Transposed Convolutional Neural Network Processor for Region of Interest Based Image Segmentation

Cited by 31 publications

References 27 publications

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

DepFiN: A 12-nm Depth-First, High-Resolution CNN Processor for IO-Efficient Inference

VG-DropDNet a Robust Architecture for Blood Vessels Segmentation on Retinal Image

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