Tensor Product and Tensor-Singular Value Decomposition Based Multi-Exposure Fusion of Images

Xu, Haiyong; Jiang, Gangyi; Yu, Mei; Zhu, Zhongkui; Bai, Ye; Song, Yang; Sun, Huifang

doi:10.1109/tmm.2021.3106789

Cited by 8 publications

(2 citation statements)

References 55 publications

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“…The computational flexibility of TCUs makes them very suitable for all applications based on AI, such as scientific computing, cryptography [6], image and video processing [5,7], virtual reality [8], the Internet of Things (IoT) and the Internet of Multimedia Things (IoMT) [9], wireless communication [10], multidimensional processing data [11][12][13][14][15][16][17][18] , and complex environment data processing [19]. In fact, several works have proposed methods to increase the accuracy and performance of parallel algorithms using tensor-based models [11].…”

Section: Introductionmentioning

confidence: 99%

Exploring Hardware Fault Impacts on Different Real Number Representations of the Structural Resilience of TCUs in GPUs

Limas Sierra,

Guerrero-Balaguera,

Condia

et al. 2024

Electronics

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The most recent generations of graphics processing units (GPUs) boost the execution of convolutional operations required by machine learning applications by resorting to specialized and efficient in-chip accelerators (Tensor Core Units or TCUs) that operate on matrix multiplication tiles. Unfortunately, modern cutting-edge semiconductor technologies are increasingly prone to hardware defects, and the trend to highly stress TCUs during the execution of safety-critical and high-performance computing (HPC) applications increases the likelihood of TCUs producing different kinds of failures. In fact, the intrinsic resiliency to hardware faults of arithmetic units plays a crucial role in safety-critical applications using GPUs (e.g., in automotive, space, and autonomous robotics). Recently, new arithmetic formats have been proposed, particularly those suited to neural network execution. However, the reliability characterization of TCUs supporting different arithmetic formats was still lacking. In this work, we quantitatively assessed the impact of hardware faults in TCU structures while employing two distinct formats (floating-point and posit) and using two different configurations (16 and 32 bits) to represent real numbers. For the experimental evaluation, we resorted to an architectural description of a TCU core (PyOpenTCU) and performed 120 fault simulation campaigns, injecting around 200,000 faults per campaign and requiring around 32 days of computation. Our results demonstrate that the posit format of TCUs is less affected by faults than the floating-point one (by up to three orders of magnitude for 16 bits and up to twenty orders for 32 bits). We also identified the most sensible fault locations (i.e., those that produce the largest errors), thus paving the way to adopting smart hardening solutions.

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Section: Introductionmentioning

confidence: 99%

Exploring Hardware Fault Impacts on Different Real Number Representations of the Structural Resilience of TCUs in GPUs

Limas Sierra,

Guerrero-Balaguera,

Condia

et al. 2024

Electronics

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“…This technique uses weight map extraction based on linear embedding and watershed masking. Xu et al ( 2021 ). Proposed a new multi exposure image fusion method based on tensor product and tensor singular value decomposition.…”

Section: Introductionmentioning

confidence: 99%

Multi-Exposure Image Fusion Algorithm Based on Improved Weight Function

Wang

Xiao

et al. 2022

Front. Neurorobot.

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High-dynamic-range (HDR) image has a wide range of applications, but its access is limited. Multi-exposure image fusion techniques have been widely concerned because they can obtain images similar to HDR images. In order to solve the detail loss of multi-exposure image fusion (MEF) in image reconstruction process, exposure moderate evaluation and relative brightness are used as joint weight functions. On the basis of the existing Laplacian pyramid fusion algorithm, the improved weight function can capture the more accurate image details, thereby making the fused image more detailed. In 20 sets of multi-exposure image sequences, six multi-exposure image fusion methods are compared in both subjective and objective aspects. Both qualitative and quantitative performance analysis of experimental results confirm that the proposed multi-scale decomposition image fusion method can produce high-quality HDR images.

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Analyzing the Reliability of TCUs Through Micro-architecture and Structural Evaluations for Two Real Number Formats

Limas Sierra,

Guerrero-Balaguera,

Rodriguez Condia

et al. 2024

IFIP Advances in Information and Communication Technology

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Tensor Product and Tensor-Singular Value Decomposition Based Multi-Exposure Fusion of Images

Cited by 8 publications

References 55 publications

Exploring Hardware Fault Impacts on Different Real Number Representations of the Structural Resilience of TCUs in GPUs

Exploring Hardware Fault Impacts on Different Real Number Representations of the Structural Resilience of TCUs in GPUs

Multi-Exposure Image Fusion Algorithm Based on Improved Weight Function

Analyzing the Reliability of TCUs Through Micro-architecture and Structural Evaluations for Two Real Number Formats

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