Bit-Plane Extracted Moving-Object Detection Using Memristive Crossbar-CAM Arrays for Edge Computing Image Devices

Notice of Retraction: Variability Analysis of Memristor-based Sigmoid Function

“…The main advantage of this model is in ability of programming limits of sigmoid by adjusting two bias currents [8]. As the memristor is a promising solution used in various architectures [9], [10], [11], [12], [13], [14], introducing memristors further improves the circuit is terms of on-chip area and power dissipation. This paper is structured in the following way: Section 1 gives an introduction to the article, Section 2 goes through the background information about the existing projects, in the third section Methodology is discussed, Results and Discussions are provided in the fourth section, and finally Conclusion will be drawn based on results and performed work.…”

Notice of Retraction: CMOS-Memristive Analog Multiplier Design

“…However this is not the case with memristors, since memristors can retain the data. The second advantage is memristor based circuits consume less power [4], [8]- [10], [13]. Third advantage according to [12] is that memristor based circuits operate faster.…”

Neuromemristive Circuits for Edge Computing: A Review

“…In this scheme, 1T1M and 2T1M allow to disconnect the memristors which are not involved in the update process completely and eliminate the leakage currents and effect on those memristors. To speed up the learning process, memristors in a crossbar can be updated in 2 steps: 1) update all memristive weights requiring the change from R ON to R OF F , and 2) update the others requiring the change from R OF F to R ON [133]. This method can be more efficient for the small crossbars with negligible leakage current and for modular crossbar approach, which is proposed to reduce the leakage currents in the memristive crossbar by dividing a large crossbar into smaller sub-crossbars [134], where all sub-crossbars can be updated in parallel reducing the training time.…”