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Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges.

, , , , , , and . ISVLSI, page 553-559. IEEE, (2019)

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MACISH: Designing Approximate MAC Accelerators With Internal-Self-Healing., , , , , and . IEEE Access, (2019)Area-optimized low-latency approximate multipliers for FPGA-based hardware accelerators., , , , , , and . DAC, page 159:1-159:6. ACM, (2018)Security for Machine Learning-Based Systems: Attacks and Challenges During Training and Inference., , , and . FIT, page 327-332. IEEE Computer Society, (2018)FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning., , , , and . CoRR, (2018)SNN under Attack: are Spiking Deep Belief Networks vulnerable to Adversarial Examples?, , , , , and . CoRR, (2019)ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining., , , , and . CoRR, (2019)X-DNNs: Systematic Cross-Layer Approximations for Energy-Efficient Deep Neural Networks., , , , , and . J. Low Power Electronics, 14 (4): 520-534 (2018)Robustness for Smart Cyber Physical Systems and Internet-of-Things: From Adaptive Robustness Methods to Reliability and Security for Machine Learning., , , , and . ISVLSI, page 581-586. IEEE Computer Society, (2018)Error resilience analysis for systematically employing approximate computing in convolutional neural networks., , and . DATE, page 913-916. IEEE, (2018)Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges., , , , , , and . ISVLSI, page 553-559. IEEE, (2019)