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A Review of In-Memory Computing Architectures for Machine Learning Applications.

, , , , and . ACM Great Lakes Symposium on VLSI, page 89-94. ACM, (2020)

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Adversarial Attacks Against Machine Learning-Based Resource Provisioning Systems., , , , , , , and . IEEE Micro, 43 (5): 35-44 (September 2023)Efficient Hardware Malware Detectors That are Resilient to Adversarial Evasion., , , , and . IEEE Trans. Computers, 71 (11): 2872-2887 (2022)Repttack: Exploiting Cloud Schedulers to Guide Co-Location Attacks., , , , , , , and . NDSS, The Internet Society, (2022)Lower Voltage for Higher Security: Using Voltage Overscaling to Secure Deep Neural Networks., , and . ICCAD, page 1-9. IEEE, (2021)Evolution of Defenses against Transient-Execution Attacks., , , and . ACM Great Lakes Symposium on VLSI, page 169-174. ACM, (2020)Cloak & Co-locate: Adversarial Railroading of Resource Sharing-based Attacks on the Cloud., , , , , , and . SEED, page 1-13. IEEE, (2021)Evasive Hardware Trojan through Adversarial Power Trace., , and . CoRR, (2024)Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks., , , , , , and . CoRR, (2022)Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling., , and . CoRR, (2021)SPECCFI: Mitigating Spectre Attacks using CFI Informed Speculation., , , , and . CoRR, (2019)