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Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices., , , , , , , , , and . CoRR, (2024)FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data., , , , , , , , , and 37 other author(s). CoRR, (2023)Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks., , , , , , , , and . DAC, page 1-6. IEEE, (2020)On the Viability of Using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators., , , and . SOCC, page 1-6. IEEE, (2023)FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems., , , , , , , , , and 37 other author(s). Inf. Fusion, (2024)Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators Through Training with Right-Censored Gaussian Noise., , , , and . ICCAD, page 1-9. IEEE, (2023)Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?, , and . ICCAD, page 87:1-87:9. ACM, (2022)Uncertainty Modeling of Emerging Device based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search., , , and . ASP-DAC, page 859-864. ACM, (2021)When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies., , , , , and . ASP-DAC, page 163-168. IEEE, (2020)TSB: Tiny Shared Block for Efficient DNN Deployment on NVCIM Accelerators., , , , , and . CoRR, (2024)