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Advanced high-k dielectric stacks with polySi and metal gates: Recent progress and current challenges., , and . IBM J. Res. Dev., 50 (4-5): 387-410 (2006)Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference., , , , , , , , , and 1 other author(s). CoRR, (2023)Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators., , , , , , , , , and 3 other author(s). CoRR, (2023)3D GPU architecture using cache stacking: Performance, cost, power and thermal analysis., , , , and . ICCD, page 254-259. IEEE Computer Society, (2009)Interface engineering of Si1-xGex gate stacks for high performance dual channel CMOS., , , , , , , , , and 1 other author(s). ASICON, page 573-576. IEEE, (2017)PBTI in InGaAs MOS capacitors with Al2O3/HfO2/TiN gate stacks: Interface-state generation., , , , and . IRPS, page 5. IEEE, (2018)In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory., , , , , , , , , and 8 other author(s). CoRR, (2022)Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited)., , , , , , , , , and 11 other author(s). IRPS, page 1-10. IEEE, (2023)Gradient descent-based programming of analog in-memory computing cores., , , , , , , , , and 4 other author(s). CoRR, (2023)A Quantitative Evaluation Framework for Missing Value Imputation Algorithms., , , , , and . CoRR, (2013)