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On the challenges in programming mixed-precision deep neural networks.

, , , , and . MAPL@PLDI, page 20-28. ACM, (2020)

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Efficient Weight Reuse for Large LSTMs., , , , , , and . ASAP, page 17-24. IEEE, (2019)Toward Full-Stack Acceleration of Deep Convolutional Neural Networks on FPGAs., , , , , and . IEEE Trans. Neural Networks Learn. Syst., 33 (8): 3974-3987 (2022)High-Performance Acceleration of 2-D and 3-D CNNs on FPGAs Using Static Block Floating Point., , , , and . IEEE Trans. Neural Networks Learn. Syst., 34 (8): 4473-4487 (August 2023)A Real-Time Object Detection Accelerator with Compressed SSDLite on FPGA., , , , , , , and . FPT, page 14-21. IEEE, (2018)Optimizing Reconfigurable Recurrent Neural Networks., , , , , , , and . FCCM, page 10-18. IEEE, (2020)Memory-Efficient Architecture for Accelerating Generative Networks on FPGA., , , , , , , and . FPT, page 30-37. IEEE, (2018)Towards Overlay-based Rapid In-Circuit Tuning of Deep Learning Designs., , , , , and . FPT, page 301. IEEE, (2020)A Reconfigurable Multithreaded Accelerator for Recurrent Neural Networks., , , , , , , and . FPT, page 20-28. IEEE, (2020)Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator., , , , , , and . ASP-DAC, page 250-255. IEEE, (2022)Simple and effective complementary label learning based on mean square error loss., , , , and . Mach. Vis. Appl., 34 (6): 118 (November 2023)