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DeeDiff: Dynamic Uncertainty-Aware Early Exiting for Accelerating Diffusion Model Generation.

, , , , , and . CoRR, (2023)

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E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs., , , , , , , , , and 1 other author(s). CoRR, (2018)Dynamic Reconfiguration of Thermoelectric Generators for Vehicle Radiators Energy Harvesting Under Location-Dependent Temperature Variations., , , , , , , , , and . IEEE Trans. Very Large Scale Integr. Syst., 26 (7): 1241-1253 (2018)DeeDiff: Dynamic Uncertainty-Aware Early Exiting for Accelerating Diffusion Model Generation., , , , , and . CoRR, (2023)Dynamic Gradient Balancing for Enhanced Adversarial Attacks on Multi-Task Models., , , , and . CoRR, (2023)Creating a Dataset for High-Performance Computing Code Translation: A Bridge Between HPC Fortran and C++., , , , and . CoRR, (2023)Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference., , , and . CoRR, (2024)REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs., , , , , and . FPGA, page 33-42. ACM, (2019)Softmax Regression Design for Stochastic Computing Based Deep Convolutional Neural Networks., , , , , , , , and . ACM Great Lakes Symposium on VLSI, page 467-470. ACM, (2017)Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++., , , , and . HPEC, page 1-7. IEEE, (2023)Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices., , , , , and . DATE, page 921-926. IEEE, (2022)