%0 Conference Paper %1 ff7924c8023442ffa1238839a8e211d5 %A Kalms, Lester %A Nickel, Matthias %A Göhringer, Diana %B Applied Reconfigurable Computing. Architectures, Tools, and Applications - 19th International Symposium, ARC 2023, Proceedings %C Germany %D 2023 %E Palumbo, Francesca %E Keramidas, Georgios %E Voros, Nikolaos %E Diniz, Pedro C. %I Springer Science and Business Media B.V. %K topic_federatedlearn Computer FIS_scads FPGA, Framework, HLS, OpenVX Vision, %P 97--112 %R 10.1007/978-3-031-42921-7_7 %T ArcvaVX: OpenVX Framework for Adaptive Reconfigurable Computer Vision Architectures %U http://arcsymposium.org/ %X The field of computer vision is steadily growing in its complexity and application areas. FPGAs have shown that they can meet the growing demands for performance and energy efficiency. However, their programmability is a major challenge for software programmers. With OpenVX a standard for cross platform acceleration of computer vision applications exists. Existing OpenVX FPGA frameworks often contain non-standard constructs and consider either fixed processor architectures or specialized non-adaptive accelerators. Therefore, we propose ArcvaVX, a framework that generates a runtime-adaptive vision architecture from OpenVX applications, which is performant and flexible. It (1) verifies the user implemented OpenVX applications, partitions them into task graphs and creates their meta-data (2) maps these tasks to physical nodes, creates a schedule, and clusters and places the nodes within a partition-based topology (3) creates the hardware architecture, including additional components required to generate a runtime-adaptive system. These components contain runtime configurable network adapters that can prevent deadlocks, a controller for direct memory access, and a manager that configures both and maintains the schedule. The architecture is designed for applications with high data rates and low synchronization overhead. The evaluation shows a low latency overhead of 0.006% added by the architecture, while resource consumption is more than halved compared to a design consisting only of accelerators. %@ 9783031429200

@inproceedings{ff7924c8023442ffa1238839a8e211d5, abstract = {The field of computer vision is steadily growing in its complexity and application areas. FPGAs have shown that they can meet the growing demands for performance and energy efficiency. However, their programmability is a major challenge for software programmers. With OpenVX a standard for cross platform acceleration of computer vision applications exists. Existing OpenVX FPGA frameworks often contain non-standard constructs and consider either fixed processor architectures or specialized non-adaptive accelerators. Therefore, we propose ArcvaVX, a framework that generates a runtime-adaptive vision architecture from OpenVX applications, which is performant and flexible. It (1) verifies the user implemented OpenVX applications, partitions them into task graphs and creates their meta-data (2) maps these tasks to physical nodes, creates a schedule, and clusters and places the nodes within a partition-based topology (3) creates the hardware architecture, including additional components required to generate a runtime-adaptive system. These components contain runtime configurable network adapters that can prevent deadlocks, a controller for direct memory access, and a manager that configures both and maintains the schedule. The architecture is designed for applications with high data rates and low synchronization overhead. The evaluation shows a low latency overhead of 0.006% added by the architecture, while resource consumption is more than halved compared to a design consisting only of accelerators.}, added-at = {2024-11-28T16:27:18.000+0100}, address = {Germany}, author = {Kalms, Lester and Nickel, Matthias and G{\"o}hringer, Diana}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/21c412d811a6a8126886e58ef592cc1b2/scadsfct}, booktitle = {Applied Reconfigurable Computing. Architectures, Tools, and Applications - 19th International Symposium, ARC 2023, Proceedings}, doi = {10.1007/978-3-031-42921-7_7}, editor = {Palumbo, Francesca and Keramidas, Georgios and Voros, Nikolaos and Diniz, {Pedro C.}}, interhash = {71c22fe92ccea9ab354cfc9cde7df112}, intrahash = {1c412d811a6a8126886e58ef592cc1b2}, isbn = {9783031429200}, keywords = {topic_federatedlearn Computer FIS_scads FPGA, Framework, HLS, OpenVX Vision,}, language = {English}, note = {Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 19th International Symposium on Applied Reconfigurable Computing, ARC 2023 ; Conference date: 27-09-2023 Through 29-09-2023}, pages = {97--112}, publisher = {Springer Science and Business Media B.V.}, series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, timestamp = {2024-11-28T17:41:03.000+0100}, title = {ArcvaVX: OpenVX Framework for Adaptive Reconfigurable Computer Vision Architectures}, url = {http://arcsymposium.org/}, year = 2023 }