کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946959 | 1439561 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Energy consumption analysis for the read and write mode of the memristor with voltage threshold in the real-time control system
ترجمه فارسی عنوان
تجزیه و تحلیل مصرف انرژی برای حالت خواندن و نوشتن ممریستور با آستانه ولتاژ در سیستم کنترل زمان واقعی
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کلمات کلیدی
ممریستور با آستانه ولتاژ حالت خواندن و نوشتن، مصرف انرژی، سیستم کنترل زمان واقعی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
In this paper, the hardware circuit of the memristor with voltage threshold control system (abbreviated as MVTCS) adapted to the real-time control system (abbreviated as RTCS) is designed based on the weight modification process of the memristor nerve morphology circuit. A novel forced erase mode, called no read, high voltage write (abbreviated as NR-HVW), is proposed to eliminate the need for a read process and to conduct the write process into the memristor. The authors combine the low voltage read and high voltage write (abbreviated as LVR-HVW) and the high voltage read and high voltage write (abbreviated as HVR-HVW) modes, and then compare and analyze the advantages and disadvantages of the three read and write modes from the perspective of energy consumption. The numerical simulation technology is used to verify the design proposed by the author, and the simulation results demonstrate that the LVR-HVW mode has the lowest energy consumption, the HVR-HVW mode comes the second, and the NR-HVW mode is the highest. However, the NR-HVW mode can meet high-precision requirements for the RTCS without testing equipment. The research of this paper is believed to provide some technical support for the application of the memristor with voltage threshold (abbreviated as MVT) in the RTCS.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 266, 29 November 2017, Pages 477-484
Journal: Neurocomputing - Volume 266, 29 November 2017, Pages 477-484
نویسندگان
Liu Jun, Li Tianshu, Duan Shukai, Wang Lidan,