標題: Titlebook: Benchmarking, Measuring, and Optimizing; Second BenchCouncil Wanling Gao,Jianfeng Zhan,Dan Stanzione Conference proceedings 2020 Springer [打印本頁] 作者: Randomized 時間: 2025-3-21 19:23
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書目名稱Benchmarking, Measuring, and Optimizing影響因子(影響力)學科排名
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書目名稱Benchmarking, Measuring, and Optimizing網(wǎng)絡(luò)公開度學科排名
書目名稱Benchmarking, Measuring, and Optimizing被引頻次
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書目名稱Benchmarking, Measuring, and Optimizing讀者反饋
書目名稱Benchmarking, Measuring, and Optimizing讀者反饋學科排名
作者: 口音在加重 時間: 2025-3-21 21:41 作者: Bouquet 時間: 2025-3-22 04:02
Early Experience in Benchmarking Edge AI Processors with Object Detection?Workloadse applications, especially for Edge Computing scenarios, due to its high power consumption and high cost. Thus, researchers and engineers have spent a lot of effort on designing edge-side artificial intelligence (AI) processors recently. Because of different edge-side application requirements, edge 作者: 皮薩 時間: 2025-3-22 04:34 作者: Foam-Cells 時間: 2025-3-22 11:23 作者: 預(yù)示 時間: 2025-3-22 14:28
Exploring the Performance Bound of Cambricon Accelerator in End-to-End Inference Scenarioient hardware accelerators for machine learning, especially for deep learning, covering from edge embedded devices to cloud data centers. However, in the real application scenario, the complicated software stack and the extra overhead (memory copy) hinder the full exploitation of the accelerator per作者: condemn 時間: 2025-3-22 17:27
Improve Image Classification by Convolutional Network on Cambricon issue. In this paper, we exploit, evaluate and validate the performance of the ResNet101 image classification network on Cambricon with Cambricon Caffe framework, demonstrating the availability and ease of use of this system. Experiments with various operational modes and the processes of model inf作者: ACRID 時間: 2025-3-23 01:05
RVTensor: A Light-Weight Neural Network Inference Framework Based on the RISC-V Architectureas attracted the attention of IoT vendors. However, research on the IoT scenario inference framework based on the RISC-V architecture is rare. Popular frame-works such as MXNet, TensorFlow, and Caffe are based on the X86 and ARM architectures, and they are not optimized for the IoT scenarios. We pro作者: 革新 時間: 2025-3-23 04:33 作者: maudtin 時間: 2025-3-23 06:45 作者: DEMN 時間: 2025-3-23 10:15
The Implementation and Optimization of Matrix Decomposition Based Collaborative Filtering Task on X8from the big dataset in daily lives. Collaborative filtering is a popular technology often used in recommendation systems, which recommend items to users according to other users having the similar behaviors with the target user or according to the items having the alike properties with the target i作者: epidermis 時間: 2025-3-23 16:32
An Efficient Implementation of the ALS-WR Algorithm on x86 CPUsvies, games, online shopping, and so on, to solve information redundancy and effectively to recommend interesting products for users. In this paper, we implement and accelerate the Alternating-Least-Squares with Weighted-.-Regularization (ALS-WR) by adopting a two-level parallel strategies on the x8作者: nonchalance 時間: 2025-3-23 19:34
Accelerating Parallel ALS for?Collaborative Filtering on Hadoopsed in CF models to calculate the latent factor matrix factorization. Parallel ALS on Hadoop is widely used in the era of big data. However, existing work on the computational efficiency of parallel ALS on Hadoop have two defects. One is the imbalance of data distribution, the other is lacking the f作者: 羽飾 時間: 2025-3-23 22:23
Improving RGB-D Face Recognition via Transfer Learning from a Pretrained 2D Networktive to variations in poses, facial expressions and illuminations. Depth images provide valuable information to help model facial boundaries and understand the global facial layout and provide low frequency patterns. Intuitively, RGB-D images are more robust to external environments than RGB images.作者: Prostatism 時間: 2025-3-24 03:21 作者: unstable-angina 時間: 2025-3-24 06:36 作者: neolith 時間: 2025-3-24 13:03 作者: Inflamed 時間: 2025-3-24 16:47
RVTensor: A Light-Weight Neural Network Inference Framework Based on the RISC-V Architecturepose RVTensor that a light-weight neural network inference framework based on the RISC-V architecture. RVTensor is based on the SERVE.r platform and is optimized for resource-poor scenarios. Our experiments demonstrate that the accuracy of RVTensor and the Keras is the same.作者: 招惹 時間: 2025-3-24 19:03 作者: 停止償付 時間: 2025-3-24 23:30
0302-9743 IBench; AI Challenges on X86 using AIBench; AI Challenges on 3D Face Recognition using AIBench; Benchmark; AI and Edge; Big Data; Datacenter; Performance Analysis; Scientific Computing..978-3-030-49555-8978-3-030-49556-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 逢迎白雪 時間: 2025-3-25 04:49
Conference proceedings 2020papers are organized in topical sections named: Best Paper Session; AI Challenges on Cambircon using AIBenc; AI Challenges on RISC-V using AIBench; AI Challenges on X86 using AIBench; AI Challenges on 3D Face Recognition using AIBench; Benchmark; AI and Edge; Big Data; Datacenter; Performance Analysis; Scientific Computing..作者: mastoid-bone 時間: 2025-3-25 08:18 作者: 意外 時間: 2025-3-25 15:19 作者: 強制令 時間: 2025-3-25 18:19
Infectious Diseases and Arthropodspose RVTensor that a light-weight neural network inference framework based on the RISC-V architecture. RVTensor is based on the SERVE.r platform and is optimized for resource-poor scenarios. Our experiments demonstrate that the accuracy of RVTensor and the Keras is the same.作者: 江湖郎中 時間: 2025-3-25 20:01 作者: peak-flow 時間: 2025-3-26 00:49
Infectious Diseases and Arthropodsamming models, CUDA?and OpenACC. We present the influence of the programming model on the performance and scaling characteristics. We also leverage the insights of the Roofline Scaling Trajectory analysis to tune some of the NAS Parallel Benchmarks, achieving up?to 2. speedup.作者: CHAFE 時間: 2025-3-26 06:37
Infectious Diseases and Nanomedicine I to enabling deep learning inference on RISC-V. Experimental results show that in our work, there is a great gap between the performance of deep learning inference on RISC-V and that on x86; thus compared with direct compilation on RISC-V, cross-compilation on x86 is a better option to significantly improve development efficiency.作者: 積云 時間: 2025-3-26 11:03 作者: Aviary 時間: 2025-3-26 16:43
Performance Analysis of GPU Programming Models Using the Roofline Scaling Trajectoriesamming models, CUDA?and OpenACC. We present the influence of the programming model on the performance and scaling characteristics. We also leverage the insights of the Roofline Scaling Trajectory analysis to tune some of the NAS Parallel Benchmarks, achieving up?to 2. speedup.作者: Obstacle 時間: 2025-3-26 19:39
AIRV: Enabling Deep Learning Inference on RISC-V to enabling deep learning inference on RISC-V. Experimental results show that in our work, there is a great gap between the performance of deep learning inference on RISC-V and that on x86; thus compared with direct compilation on RISC-V, cross-compilation on x86 is a better option to significantly improve development efficiency.作者: 偏見 時間: 2025-3-26 23:19 作者: scoliosis 時間: 2025-3-27 02:56
0302-9743 19, held in Denver, CO, USA, in November 2019...The 20 full papers and 11 short papers presented were carefully reviewed and selected from 79 submissions...The papers are organized in topical sections named: Best Paper Session; AI Challenges on Cambircon using AIBenc; AI Challenges on RISC-V using A作者: Vo2-Max 時間: 2025-3-27 08:44
https://doi.org/10.1007/978-81-322-1774-9ccess. Thus, we propose a solution for accelerating the ALS-WR algorithm by exploiting parallelism, sparsity and locality on x86 platforms. Our PSL can process 20 million ratings and the speedup using multi-threading is up?to 14.5. on a 20-core machine.作者: 表臉 時間: 2025-3-27 09:45 作者: 貧困 時間: 2025-3-27 17:28
Infectious Diseases and Arthropodsis methodology and performance numbers, we have some key observations and implications that are valuable for the future DL hardware and software co-design. Furthermore, we explore the upper bound of MLU100 inference performance under the standard ResNet-50 model and CIFAR-10 dataset.作者: 陳舊 時間: 2025-3-27 20:16 作者: 報復(fù) 時間: 2025-3-27 23:02 作者: 珊瑚 時間: 2025-3-28 05:02 作者: 挑剔為人 時間: 2025-3-28 08:33 作者: muffler 時間: 2025-3-28 11:23
https://doi.org/10.1007/978-3-030-49556-5artificial intelligence; computer hardware; computer programming; computer systems; computer vision; data作者: 業(yè)余愛好者 時間: 2025-3-28 17:01 作者: 幸福愉悅感 時間: 2025-3-28 21:30 作者: obstruct 時間: 2025-3-29 01:13
Imaginary Insect or Mite Infestationsescribed dependencies between different entities as edges. Today, a lot of graph computing systems emerge with massive diverse graph applications deployed, evaluating graph computing systems become a challenge work. Existing graph computing benchmarks are constructed with prevalent graph computing a作者: 圖表證明 時間: 2025-3-29 04:52
Miscellaneous Vector-Borne Diseasese applications, especially for Edge Computing scenarios, due to its high power consumption and high cost. Thus, researchers and engineers have spent a lot of effort on designing edge-side artificial intelligence (AI) processors recently. Because of different edge-side application requirements, edge 作者: 尖 時間: 2025-3-29 09:12 作者: headlong 時間: 2025-3-29 13:35
Infectious Diseases and Arthropodschniques, such as data parallelism, model parallelism, data pipeline, weights pruning and quantization have been proposed to accelerate the inference phase of DL workloads. However, there is still lack of a comparison of these optimization techniques to show their performance difference on dedicated作者: consent 時間: 2025-3-29 18:20 作者: oxidant 時間: 2025-3-29 20:52
Necrotic Arachnidism: Brown Recluse Bites issue. In this paper, we exploit, evaluate and validate the performance of the ResNet101 image classification network on Cambricon with Cambricon Caffe framework, demonstrating the availability and ease of use of this system. Experiments with various operational modes and the processes of model inf作者: Maximize 時間: 2025-3-30 02:52
Infectious Diseases and Arthropodsas attracted the attention of IoT vendors. However, research on the IoT scenario inference framework based on the RISC-V architecture is rare. Popular frame-works such as MXNet, TensorFlow, and Caffe are based on the X86 and ARM architectures, and they are not optimized for the IoT scenarios. We pro作者: cacophony 時間: 2025-3-30 04:14
Infectious Diseases and Nanomedicine Intelligence (AI) applications have been extensively deployed in cloud, edge, mobile and IoT devices due to latest breakthroughs in deep learning algorithms and techniques. Therefore, there is an increasing need for enabling deep learning inference on RISC-V. However, at present mainstream machine le作者: Diaphragm 時間: 2025-3-30 09:38 作者: annexation 時間: 2025-3-30 16:23 作者: 無效 時間: 2025-3-30 19:00 作者: 表皮 時間: 2025-3-30 22:10
https://doi.org/10.1007/978-981-10-7572-8sed in CF models to calculate the latent factor matrix factorization. Parallel ALS on Hadoop is widely used in the era of big data. However, existing work on the computational efficiency of parallel ALS on Hadoop have two defects. One is the imbalance of data distribution, the other is lacking the f作者: 食料 時間: 2025-3-31 04:55
Kaniz Fatema Nipa,Linda J. S. Allentive to variations in poses, facial expressions and illuminations. Depth images provide valuable information to help model facial boundaries and understand the global facial layout and provide low frequency patterns. Intuitively, RGB-D images are more robust to external environments than RGB images.作者: Spongy-Bone 時間: 2025-3-31 06:08
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/183393.jpg作者: contradict 時間: 2025-3-31 11:50
