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What is Enabling Real-Time Object Detection On Low Cost FPGAs about?

This research paper presents a hardware accelerator for real-time object detection using convolutional neural networks (CNNs) on low-cost FPGAs, specifically optimizing the tiny-YOLOv2 model. It introduces two metrics, DSPefficiency and a new Costefficiency, to evaluate and enhance the performance and commercial viability of FPGA implementations. The results demonstrate that the proposed architecture achieves a DSPefficiency of 90% and a Costefficiency of 0.146, outperforming existing state-of-the-art solut

Author

Giri Prasanth

Language

EN