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RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks.

, , , , , and . IEEE Access, (2022)

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Automated Search-Based Functional Approximation for Digital Circuits., , and . Approximate Circuits, Springer, (2019)Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques., , , , and . IEEE Trans. Very Large Scale Integr. Syst., 27 (12): 2734-2744 (2019)autoXFPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems., , , , and . CoRR, (2023)Satisfiability Solving Meets Evolutionary Optimisation in Designing Approximate Circuits., , , and . SAT, volume 12178 of Lecture Notes in Computer Science, page 481-491. Springer, (2020)ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining., , , , and . ICCAD, page 1-8. ACM, (2019)Role of circuit representation in evolutionary design of energy-efficient approximate circuits., , and . IET Comput. Digit. Tech., 12 (4): 139-149 (2018)Designing Approximate Arithmetic Circuits with Combined Error Constraints., , , and . DSD, page 785-792. IEEE, (2022)ADAC: Automated Design of Approximate Circuits., , , , , and . CAV (1), volume 10981 of Lecture Notes in Computer Science, page 612-620. Springer, (2018)ArithsGen: Arithmetic Circuit Generator for Hardware Accelerators., and . DDECS, page 44-47. IEEE, (2022)Optimization of BDD-based Approximation Error Metrics Calculations.. ISVLSI, page 86-91. IEEE, (2022)