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Dependability of Alternative Computing Paradigms for Machine Learning: hype or hope?, , , , , , , and . DDECS, page 7-13. IEEE, (2022)Exploiting Approximate Computing for Efficient and Reliable Convolutional Neural Networks., , and . ISVLSI, page 326. IEEE, (2022)A Low-Cost Reliability vs. Cost Trade-Off Methodology to Selectively Harden Logic Circuits., , , , , and . J. Electron. Test., 33 (1): 25-36 (2017)Reducing Overprovision of Triple Modular Reduncancy Owing to Approximate Computing., , , and . IOLTS, page 1-7. IEEE, (2021)A low-cost susceptibility analysis methodology to selectively harden logic circuits., , , , , and . ETS, page 1-2. IEEE, (2016)Artificial neural network-based solution for PSP MOSFET model card extraction., , , , , , , , and . VLSID, page 6-12. IEEE, (2024)Input-aware accuracy characterization for approximate circuits., , , , , , , and . DSN-W, page 179-182. IEEE, (2023)Energy-efficient Computation-In-Memory Architecture using Emerging Technologies., , , , , , , , , and 2 other author(s). ICM, page 325-334. IEEE, (2023)Investigating the effect of approximate multipliers on the resilience of a systolic array DNN accelerator., , , , , , and . DFT, page 1-6. IEEE, (2023)QAMR: an Approximation-Based Fully Reliable TMR Alternative for Area Overhead Reduction., , , and . ETS, page 1-6. IEEE, (2020)