Titlebook: Applied Reconfigurable Computing. Architectures, Tools, and Applications; 19th International S Francesca Palumbo,Georgios Keramidas,Pedro C

絕食

法律的瑕疵

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

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.

意見(jiàn)一致

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

不足的東西

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

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

平靜生活

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