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FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference., , , , , , and . CoRR, (2021)Rumba: an online quality management system for approximate computing., , , and . ISCA, page 554-566. ACM, (2015)Post-silicon bug diagnosis with inconsistent executions., , and . ICCAD, page 755-761. IEEE Computer Society, (2011)Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale., , , , , , , , , and 8 other author(s). CoRR, (2021)Efficient Soft-Error Detection for Low-precision Deep Learning Recommendation Models., , , , , , and . IEEE Big Data, page 1556-1563. IEEE, (2022)Efficient Soft-Error Detection for Low-precision Deep Learning Recommendation Models., , , , , , and . CoRR, (2021)Low cost control flow protection using abstract control signatures., and . LCTES, page 3-12. ACM, (2013)Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications., , , , , , , , , and 18 other author(s). CoRR, (2018)Harnessing Soft Computations for Low-Budget Fault Tolerance., and . MICRO, page 319-330. IEEE Computer Society, (2014)Quality Control for Approximate Accelerators by Error Prediction., , , and . IEEE Des. Test, 33 (1): 43-50 (2016)