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Are FPGAs suffering from the innovator's dilemna?, and . FPGA, page 135-136. ACM, (2013)FPGA challenges and opportunities at 40nm and beyond.. FPL, page 4. IEEE, (2009)You Cannot Improve What You Do not Measure: FPGA vs. ASIC Efficiency Gaps for Convolutional Neural Network Inference., , and . ACM Trans. Reconfigurable Technol. Syst., 11 (3): 20:1-20:23 (2018)Toward Software-like Debugging for FPGAs via Checkpointing and Transaction-based Co-Simulation., and . ACM Trans. Reconfigurable Technol. Syst., 16 (2): 31:1-31:24 (June 2023)Tatum: Parallel Timing Analysis for Faster Design Cycles and Improved Optimization., and . FPT, page 110-117. IEEE, (2018)Specializing for Efficiency: Customizing AI Inference Processors on FPGAs., , and . ICM, page 62-65. IEEE, (2021)Tensor Slices to the Rescue: Supercharging ML Acceleration on FPGAs., , , and . FPGA, page 23-33. ACM, (2021)Koios: A Deep Learning Benchmark Suite for FPGA Architecture and CAD Research., , , , , , , , , and 2 other author(s). FPL, page 355-362. IEEE, (2021)HPIPE NX: Boosting CNN Inference Acceleration Performance with AI-Optimized FPGAs., , , and . FPT, page 1-9. IEEE, (2022)cuSCNN : an Efficient CUDA Implementation of Sparse CNNs., , , , and . HEART, page 107-113. ACM, (2023)