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Exploration and Tradeoffs of different Kernels in FPGA Deep Learning Applications., , and . ISPD, page 42-47. ACM, (2018)Neural network pruning for biomedical image segmentation., , , and . Medical Imaging: Image-Guided Procedures, volume 11598 of SPIE Proceedings, SPIE, (2021)xDNN: Inference for Deep Convolutional Neural Networks., , , , , and . ACM Trans. Reconfigurable Technol. Syst., 15 (2): 18:1-18:29 (2022)Neural network pruning and hardware acceleration., , , , and . UCC, page 440-445. IEEE, (2020)XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse., , , , , , , , , and 8 other author(s). CoRR, (2022)Separated RoadTopoFormer., , , , , and . CoRR, (2023)MLPerf Inference Benchmark., , , , , , , , , and 37 other author(s). ISCA, page 446-459. IEEE, (2020)Deep learning challenges and solutions with Xilinx FPGAs., , , and . ICCAD, page 908-913. IEEE, (2017)MLPerf Inference Benchmark., , , , , , , , , and 37 other author(s). CoRR, (2019)Precision medicine and FPGA technology: Challenges and opportunities., , , , , , and . MWSCAS, page 655-658. IEEE, (2017)