Titlebook: Benchmarking, Measuring, and Optimizing; Second BenchCouncil Wanling Gao,Jianfeng Zhan,Dan Stanzione Conference proceedings 2020 Springer

Randomized

書目名稱Benchmarking, Measuring, and Optimizing影響因子(影響力)




書目名稱Benchmarking, Measuring, and Optimizing影響因子(影響力)學(xué)科排名




書目名稱Benchmarking, Measuring, and Optimizing網(wǎng)絡(luò)公開度




書目名稱Benchmarking, Measuring, and Optimizing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Benchmarking, Measuring, and Optimizing被引頻次




書目名稱Benchmarking, Measuring, and Optimizing被引頻次學(xué)科排名




書目名稱Benchmarking, Measuring, and Optimizing年度引用




書目名稱Benchmarking, Measuring, and Optimizing年度引用學(xué)科排名




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(xué)科排名

口音在加重

Bouquet

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

皮薩

Foam-Cells

預(yù)示

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

condemn

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

ACRID

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

革新

maudtin