mlsanityData to 13 Oct 2026

A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU

arXiv 2303.08999 ↗First posted 16 Mar 2023cs.AR cs.CV eess.IVcited by 0

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Abstract (arXiv)

Recent years have witnessed impressive progress in super-resolution (SR) processing. However, its real-time inference requirement sets a challenge not only for the model design but also for the on-chip implementation. In this paper, we implement a full-stack SR acceleration framework on embedded GPU devices. The special dictionary learning algorithm used in SR models was analyzed in detail and accelerated via a novel dictionary selective strategy. Besides, the hardware programming architecture together with the model structure is analyzed to guide the optimal design of computation kernels to minimize the inference latency under the resource constraints. With these novel techniques, the communication and computation bottlenecks in the deep dictionary learning-based SR models are tackled perfectly. The experiments on the edge embedded NVIDIA NX and 2080Ti show that our method outperforms the state-of-the-art NVIDIA TensorRT significantly, and can achieve real-time performance.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 32 of its 66 references that are in the map. First posted on arXiv 16 Mar 2023.

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32 of 66 references.

References · 32

  1. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,315
  2. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,906
  3. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,221
  4. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  5. 2017Channel Pruning for Accelerating Very Deep Neural Networkscited by 701
  6. 2020Up or Down? Adaptive Rounding for Post-Training Quantizationcited by 617
  7. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 605
  8. 2015BinaryConnect: Training Deep Neural Networks with binary weights during propagationscited by 589
  9. 2017Learning Sparse Neural Networks through L0 Regularizationcited by 574
  10. 2021BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstructioncited by 472
  11. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 453
  12. 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 394
20 more
  1. 2018Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Accelerationcited by 367
  2. 2019Data-Free Quantization Through Weight Equalization and Bias Correctioncited by 333
  3. 2018Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Networkcited by 264
  4. 2017Learning a Single Convolutional Super-Resolution Network for Multiple Degradationscited by 228
  5. 2020Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluationcited by 187
  6. 2017Data-Driven Sparse Structure Selection for Deep Neural Networkscited by 181
  7. 2019Feedback Network for Image Super-Resolutioncited by 155
  8. 2020Unfolding the Alternating Optimization for Blind Super Resolutioncited by 129
  9. 2020MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolutioncited by 89
  10. 2021LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyondcited by 88
  11. 2019Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Searchcited by 66
  12. 2019FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edgecited by 48
  13. 2019JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Videocited by 31
  14. 2016RAISR: Rapid and Accurate Image Super Resolutioncited by 27
  15. 2020Journey Towards Tiny Perceptual Super-Resolutioncited by 27
  16. 2021Collapsible Linear Blocks for Super-Efficient Super Resolutioncited by 19
  17. 2017Ternary Neural Networks with Fine-Grained Quantizationcited by 17
  18. 2019Lightweight Image Super-Resolution with Adaptive Weighted Learning Networkcited by 16
  19. 2017BLADE: Filter Learning for General Purpose Computational Photographycited by 12
  20. 2020Balancing Efficiency and Flexibility for DNN Acceleration via Temporal GPU-Systolic Array Integrationcited by 8

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