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Monolithically Integrated RRAM- and CMOS-Based In-Memory Computing Optimizations for Efficient Deep Learning.

, , , , , , , , , and . IEEE Micro, 39 (6): 54-63 (2019)

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Phase Noise Impairment and Environment-Adaptable Fast (EAF) Optimization for Programming of Reconfigurable Radio Frequency (RF) Receivers., , , , , , and . GLOBECOM, page 1-6. IEEE, (2015)Minimizing Area and Energy of Deep Learning Hardware Design Using Collective Low Precision and Structured Compression., , , , , and . CoRR, (2018)Vesti: An In-Memory Computing Processor for Deep Neural Networks Acceleration., , , , , and . ACSSC, page 1516-1521. IEEE, (2019)K-Nearest Neighbor Hardware Accelerator Using In-Memory Computing SRAM., , , , and . ISLPED, page 1-6. IEEE, (2019)Improving DNN Hardware Accuracy by In-Memory Computing Noise Injection., , , , , , and . IEEE Des. Test, 39 (4): 71-80 (2022)PIMCA: A 3.4-Mb Programmable In-Memory Computing Accelerator in 28nm for On-Chip DNN Inference., , , , , , , , , and . VLSI Circuits, page 1-2. IEEE, (2021)Compact Modeling of IGZO-based CAA-FETs with Time-zero-instability and BTI Impact on Device and Capacitor-less DRAM Retention Reliability., , , , , , , , , and 7 other author(s). VLSI Technology and Circuits, page 300-301. IEEE, (2022)Accurate passivity-enforced macromodeling for RF circuits via iterative zero/pole update based on measurement data., , , , , , and . ASP-DAC, page 441-446. IEEE, (2015)XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks., , , and . IEEE J. Solid State Circuits, 55 (6): 1733-1743 (2020)Energy-Efficient Circuit and Architecture Designs for Intelligent Systems.. Arizona State University, Tempe, USA, (2020)base-search.net (ftarizonastateun:item:62917).