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Energy and bandwidth aware mapping of IPs onto regular NoC architectures using Multi-Objective Genetic Algorithms., and . SoC, page 27-31. IEEE, (2009)SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads., , , , , and . ACM Trans. Archit. Code Optim., 17 (4): 39:1-39:26 (2020)Leveraging Prediction Confidence For Versatile Optimizations to CNNs., , , , and . AIMLSystems, page 2:1-2:7. ACM, (2021)Towards a Complete Methodology for Synthesizing Bundled-Data Asynchronous Circuits on FPGAs., , , and . ISLPED, page 1-6. IEEE, (2019)Determining Optimal Coherency Interface for Many-Accelerator SoCs Using Bayesian Optimization., , , , , and . IEEE Comput. Archit. Lett., 18 (2): 119-123 (2019)Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots., , , , , , , , and . CoRR, (2021)Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy., and . CoRR, (2021)Machine Learning-Enhanced Prediction of Surface Smoothness for Inertial Confinement Fusion Target Polishing Using Limited Data., , , , , , , and . CoRR, (2023)Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute Characterization of Autonomous Unmanned Aerial Vehicles., , , , , and . ISPASS, page 162-174. IEEE, (2022)A comprehensive methodology to determine optimal coherence interfaces for many-accelerator SoCs., , , , , and . ISLPED, page 145-150. ACM, (2020)