Heidorn C, Walter D, Candir YE, Hannig F, Teich J (2021)
Publication Language: English
Publication Type: Conference contribution, Abstract of lecture
Publication year: 2021
Publisher: IEEE
Pages Range: 388
Conference Proceedings Title: Proceedings of the 31st International Conference on Field Programmable Logic and Applications (FPL)
Event location: Virtual Conference
ISBN: 978-1-6654-3759-2
DOI: 10.1109/FPL53798.2021.00079
The advent of deep learning has revolutionized the domain of computer vision. Convolutional neural networks (CNNs) became state-of-the-art for solving complex tasks thanks to technological advances of high-end accelerators, such as GPUs and FPGAs, combined in clusters or cloud solutions. In embedded systems, CNNs are also of great interest. However, often these devices cannot afford to offload computational-intensive workloads to the cloud due to strict energy or real-time constraints. Tightly Coupled Processor Arrays (TCPAs) are ideal architectures for accelerating nested loop programs at high energy efficiency. In this demonstrator, we show how TCPAs can meet these requirements at the edge of computing. For illustration, we designed a CNN-based hand sign recognition which is accelerated on a TCPA, implemented the TCPA prototypically as an overlay on a Xilinx Zynq System-on-a-Chip (SoC), and showcase tremendous speedups compared with the integrated ARM Cortex-A9 processor.
APA:
Heidorn, C., Walter, D., Candir, Y.E., Hannig, F., & Teich, J. (2021). Hand Sign Recognition via Deep Learning on Tightly Coupled Processor Arrays. Paper presentation at 31st International Conference on Field Programmable Logic and Applications (FPL), Virtual Conference.
MLA:
Heidorn, Christian, et al. "Hand Sign Recognition via Deep Learning on Tightly Coupled Processor Arrays." Presented at 31st International Conference on Field Programmable Logic and Applications (FPL), Virtual Conference IEEE, 2021.
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