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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10259 条记录，以下是331-340 订阅

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high-performance Spatial Composite 3D Printing 43

High-Performance Spatial Composite 3D Printing

引用

43rd Annual conference of the Association for computer Aided Design in Architecture: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy, ACADIA 2023

作者： Martin, Eduardo Chamorro Burry, Mark Marengo, Mathilde IAAC - Institute for Advanced Architecture of Catalonia Spain Swinburne University of Technology Australia

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Kyberator: A high-Efficiency FPGA-Based Multi-Mode CRYSTALS-Kyber Accelerator for Quantum-Resistant Security Applications 21

Kyberator: A High-Efficiency FPGA-Based Multi-Mode CRYSTALS-...

引用

21st International System-on-Chip Design conference, ISOCC 2024

作者： Dinh, Nhat Nguyen Luan Pham, Hoai Duong Le, Vu Trung Hai Vu, Tuan Tran, Van Duy Nakashima, Yasuhiko University of Information Technology - VNUHCM Viet Nam Nara Institute of Science and Technology Nara630-0192 Japan

ISBN: (纸本)9798350377088

Hardware development for CRYSTALS-Kyber is essential to prevent future quantum computer attacks. However, existing CRYSTALS-Kyber hardware often has low performance and lacks the flexibility to support the three operation modes: key generation, encryption, and decryption. To address this issue, we propose the Multi-Mode CRYSTALS-Kyber Accelerator, named Kyberator, which supports all three operation modes with high speed and hardware efficiency. Notably, this paper is the first to analyze the real-time performance at an actual system-on-chip (SoC) level on an embedded FPGA platform, demonstrating that the power-delay product of the Kyberator is 4.9 to 10.1 times greater than that of the most powerful current CPUs. Compared with related works on FPGA, the Kyberator is at least 2.1 times better in terms of area-delay product and offers better flexibility. © 2024 IEEE.

关键词： System-on-chip

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PFDM: PARSER-FREE VIRTUAL TRY-ON VIA DIFFUSION MODEL 49

PFDM: PARSER-FREE VIRTUAL TRY-ON VIA DIFFUSION MODEL

引用

49th IEEE International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Niu, Yunfang Yi, Dong Wu, Lingxiang Liu, Zhiwei Cai, Pengxiang Wang, Jinqiao Chinese Acad Sci Inst Automat Fdn Model Res Ctr Beijing Peoples R China Wuhan AI Res Wuhan Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Virtual try-on can significantly improve the garment shopping experiences in both online and in-store scenarios, attracting broad interest in computer vision. However, to achieve high-fidelity try-on performance, most state-of-the-art methods still rely on accurate segmentation masks, which are often produced by near-perfect parsers or manual labeling. To overcome the bottleneck, we propose a parser-free virtual try-on method based on the diffusion model (PFDM). Given two images, PFDM can "wear" garments on the target person seamlessly by implicitly warping without any other information. To learn the model effectively, we synthesize many pseudo-images and construct sample pairs by wearing various garments on persons. Supervised by the large-scale expanded dataset, we fuse the person and garment features using a proposed Garment Fusion Attention (GFA) mechanism. Experiments demonstrate that our proposed PFDM can successfully handle complex cases, synthesize high-fidelity images, and outperform both state-of-the-art parser-free and parser-based models.

关键词： Virtual try-on diffusion models implicit warping high-resolution image synthesis

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ChatBLAS: The First AI-Generated and Portable BLAS Library

ChatBLAS: The First AI-Generated and Portable BLAS Library

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2024 Workshops of the International conference for high performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Valero-Lara, Pedro Godoy, William F. Teranishi, Keita Balaprakash, Prasanna Vetter, Jeffrey S. Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350355543

We present ChatBLAS, the first AI-generated and portable Basic Linear Algebra Subprograms (BLAS) library on different CPU/GPU configurations. The purpose of this study is (i) to evaluate the capabilities of current large language models (LLMs) to generate a portable and HPC library for BLAS operations and (ii) to define the fundamental practices and criteria to interact with LLMs for HPC targets to elevate the trustworthiness and performance levels of the AI-generated HPC codes. The generated C/C++ codes must be highly optimized using device-specific solutions to reach high levels of performance. Additionally, these codes are very algorithm-dependent, thereby adding an extra dimension of complexity to this study. We used OpenAI's LLM ChatGPT and focused on vector-vector BLAS level-1 operations. ChatBLAS can generate functional and correct codes, achieving high-trustworthiness levels, and can compete or even provide better performance against vendor libraries. © 2024 IEEE.

关键词： high-bandwidth on-chip memory JACC Julia metaprogramming performance portability

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Image Processing-Enabled Validation for Robotic Test Automation: A Case Study on POS Devices 9

Image Processing-Enabled Validation for Robotic Test Automat...

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9th International conference on computer Science and Engineering, UBMK 2024

作者： Emektar, Mirac Kadioglu, Harun Efendioglu, Ahmet Harmanci, Fatih Mehmet Virgosol Istanbul Turkey

ISBN: (纸本)9798350365887

This study conducts an in-depth examination of the effectiveness of robotic automation systems in the testing processes of POS devices used in payment systems. It compares the shortcomings of traditional manual testing in terms of speed, accuracy, and consistency with modern automation techniques. The advantages provided by automation, such as speed, consistency, and repeatability, are critical for testing POS devices in complex, multi-stage scenarios. The study focuses on the Roboclick project, successfully completed by Virgosol under the KOSGEB R&D, Product Development, and Innovation Support program. The system developed within this project is designed not only to test the basic functions of POS devices but also to evaluate their operational processes, which arc integrated with various platforms such as web, desktop, and mobile. This system goes beyond traditional automation tools by enabling the automation of more comprehensive and in-depth test scenarios. The robotic arm-based system has been able to more effectively assess the performance, reliability, and debugging processes of POS devices. The findings indicate that robotic automation has the potential to not only accelerate test processes but also significantly enhance the accuracy and consistency of fest results. In this context, it is emphasized that robotic automation makes a substantial contribution, particularly in exccuting high-volume test scenarios and in continuous integration processes. © 2024 IEEE.

关键词： program debugging

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Characterizing open-set ATR methods to detect out of distribution data for a maritime object classifier in space-based electro optical imagery 34

Characterizing open-set ATR methods to detect out of distrib...

引用

conference on Automatic Target Recognition XXXIV

作者： Warner, John G. Patel, Vishal US Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA Johns Hopkins Univ 3400 N Charles St Baltimore MD USA

ISBN: (纸本)9781510673977;9781510673960

Reliable computer vision object classification is important for security applications that make high stakes decisions based on automated algorithms. In real world scenarios, it is often impractical to meet the implicit assumption that all relevant, labelled data may be attained prior to training. To avoid performance degradation, a recently developed open-set detection framework is applied to the classification of ships from clutter in satellite, Electro-Optical (EO) imagery and is shown to reliably identify data that is out of distribution from training data. A Binary Classifier (BC) and Category-aware Binary Classifier (CBC) model were compared to OpenMax and found to provide improvements in identifying unknown imagery. This enables an operator to know whether to believe classification results from a deep learning-based algorithm.

关键词： Open-set detection Deep Learning computer vision CNN satellite imagery Automatic Target Recognition maritime

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SparseViT: Revisiting Activation Sparsity for Efficient high-Resolution Vision Transformer

SparseViT: Revisiting Activation Sparsity for Efficient High...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Chen, Xuanyao Liu, Zhijian Tang, Haotian Yi, Li Zhao, Hang Han, Song Shanghai Qi Zhi Inst Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China MIT Cambridge MA USA

ISBN: (纸本)9798350301298

high-resolution images enable neural networks to learn richer visual representations. However, this improved performance comes at the cost of growing computational complexity, hindering their usage in latency-sensitive applications. As not all pixels are equal, skipping computations for less-important regions offers a simple and effective measure to reduce the computation. This, however, is hard to be translated into actual speedup for CNNs since it breaks the regularity of the dense convolution workload. In this paper, we introduce SparseViT that revisits activation sparsity for recent window-based vision transformers (ViTs). As window attentions are naturally batched over blocks, actual speedup with window activation pruning becomes possible: i.e., similar to 50% latency reduction with 60% sparsity. Different layers should be assigned with different pruning ratios due to their diverse sensitivities and computational costs. We introduce sparsity-aware adaptation and apply the evolutionary search to efficiently find the optimal layerwise sparsity configuration within the vast search space. SparseViT achieves speedups of 1.5x, 1.4x, and 1.3x compared to its dense counterpart in monocular 3D object detection, 2D instance segmentation, and 2D semantic segmentation, respectively, with negligible to no loss of accuracy.

关键词： Efficient and scalable vision

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The Students' performance of Digital Sustainable Development Education in Gaining Competitive Advantage in ASEAN's Business Competition in the Post-Pandemic Era 23

The Students' Performance of Digital Sustainable Development...

引用

6th International conference on Software Engineering and Information Management, ICSIM 2023

作者： Pane, Murty Magda Siregar, Christian Character Building Development Center School of Computer Science Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Character Building Development Center Mass Communication Program Communication Department Faculty of Digital Communication and Hotel and Tourism Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450398237

This study is a preliminary one aims to find out the performance of the attitudes and behaviors of Binus University students towards sustainable development to gain the competitive advantages in ASEAN business competition in the post-pandemic era. It was taken using the convenience sampling technique to 225 students from varied semester and departments. It used the quantitative method (corrected item total correlations for both variables are 0.433 and 0.419 (>0.3), α for both are 0.750 and 0.773 (> 0.5)) with Likert scale to measure the performance of agree and disagree for managing and interpreting the data. The respondents are 250 students from varied semesters and departments. The performance of the most chosen variable of attitudes are: scale 4 is chosen by 44.44% and scale 5 is chosen by 35.56% of respondents. Whilst the performance of the most chosen variable of behaviors is: scale 4 is chosen by 39.56% and scale 5 is chosen by 27.56% of respondents. These results indicated that the sustainable development awareness of Bina Nusantara students tends to the level of moderate to high. © 2023 ACM.

关键词： Students

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Dynamic Neural Radiosity with Multi-grid Decomposition 24

Dynamic Neural Radiosity with Multi-grid Decomposition

引用

2024 SIGGRAPH Asia conference

作者： Su, Rui Dong, Honghao Ren, Jierui Jin, Haqjie Chen, Yisong Wang, Guoping Li, Sheng Peking Univ Sch Comp Sci Beijing Peoples R China Peking Univ Coll Future Technol Beijing Peoples R China

ISBN: (纸本)9798400711312

Prior approaches to the neural rendering of global illumination typically rely on complex network architectures and training strategies to model the global effects. This often leads to impractically high overheads for both training and inference. The neural radiosity technique marks a significant advancement by injecting the radiometric prior into the training process, allowing for efficient modeling of the global radiance fields using a lightweight network and grid-based representations. However, this method encounters difficulties in modeling dynamic scenes, as the high-dimensional feature space quickly becomes unmanageable as the number of varying scene parameters grows. In this work, we extend neural radiosity for variable scenes through a novel neural decomposition method. To achieve this, we first parameterize the animated scene with an explicit vector v, which conditions a high-dimensional radiance field L-theta. We then develop a practical representation for L-theta by decomposing the high-dimensional feature grid into 3D grids, 2D feature planes, and lightweight MLPs. This strategy effectively models the correlation between 3D spatial features and dynamic scene variables, while maintaining a practical memory and computational cost. Experimental results show that our method facilitates efficient dynamic global illumination rendering with practical runtime performance, outperforming previous state-of-the-art techniques with both reduced training and inference costs.

关键词： Neural Rendering Radiosity Dynamic Scene high-dimensional Feature Global Illumination

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Advanced Recursive Spherical Quantization for Image Retrieval 20

Advanced Recursive Spherical Quantization for Image Retrieva...

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20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Fuchun, Wang Lei, Zhao Chaofeng, Yang Yu, Zhao Sichuan Artificial Intelligence Research Institute Yibin644000 China School of Computer Science and Technology Xinjiang University Urumqi830017 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

In recent years, quantization technology has proven to be very effective in the field of supervised image retrieval, owing to its capacity to provide both high accuracy and swift retrieval speeds. However, the challenge of balancing retrieval accuracy and speeds, as denoted by variable code lengths, persists as a fundamental consideration across different applications. Recently, there are a few methods that employ recursive strategies for quantization encoding, and there remains substantial room for improvement in their effectiveness. In this paper, we introduce the concept of a Recursive Spherical Quantization (RSQ) architecture, capable of generating sequential binary codes. It can reuse unit vector codebook to recursively quantize real vectors. Empirical findings from benchmark datasets demonstrate that our model is competitive or even superior performance in image retrieval, while simultaneously demanding a significantly reduced number of parameters. © 2023 IEEE.

关键词： Vector quantization

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