绝食
发表于 2025-3-25 04:50:17
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法律的瑕疵
发表于 2025-3-25 09:06:24
Memory-Aware Scheduling for a Resource-Elastic FPGA Operating Systemhedulers are evaluated and implemented on an Ultra96 FPGA board. The presented approaches show (on average) approximately ., ., ., and . improvements in memory throughput, task execution time, makespan time, and job throughput, respectively, over an existing state-of-the-art memory-agnostic scheduler.
招致
发表于 2025-3-25 11:58:05
FPGA-Integrated Bag of Little Bootstraps Accelerator for Approximate Database Query Processing by factor 4 without and factor 2 with data transfer time for one million entries. This improvement increases with the amount of data to be processed. Implementing the BLB algorithm on an FPGA as an approximate query processing accelerator offers a promising approach for improving database query processing.
意见一致
发表于 2025-3-25 17:43:12
A Convolution Neural Network Based Displaced Vertex Trigger for the Belle II Experimentigin of these tracks for each frame. The complete system has been successfully implemented on the FPGA platform (XCVU160) used in the experiment and meets the specified requirements of the trigger system.
Ergots
发表于 2025-3-26 00:00:30
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不足的东西
发表于 2025-3-26 02:11:18
DNN Model Theft Through Trojan Side-Channel on Edge FPGA Acceleratorcture information. Our experiments demonstrate the effectiveness of the proposed attack and highlight the need for robust security measures to protect DNN intellectual property (IP) models that are deployed on edge FPGA platforms.
歹徒
发表于 2025-3-26 07:54:58
Conference proceedings 2023rmany, in September 2023..The 18 full papers presented in this volume were reviewed and selected from numerous submissions. The proceedings also contain 4 short PhD papers. The contributions were organized in topical sections as follows: Design methods and tools; applications; architectures; special
Conjuction
发表于 2025-3-26 09:55:10
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平静生活
发表于 2025-3-26 12:40:30
Scalable and Energy-Efficient NN Acceleration with GPU-ReRAM Architecturecceleration, thus enabling ReRAM to be scalable for complex NNs while significantly reducing energy consumption. The effectiveness of this approach was tested on real-world models, resulting in a meaningful 6x reduction in energy consumption without sacrificing inference accuracy.
GIBE
发表于 2025-3-26 20:23:29
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