A novel digital logic implementation approach on nanocrossbar arrays using memristor-based multiplexers

Owlia, Hadi; Keshavarzi, Parviz; Rezai, Abdalhossein

doi:10.1016/j.mejo.2014.04.014

Cited by 42 publications

(85 citation statements)

References 37 publications

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“…A comparative summary of the memristor-based MUX realization in [8] and the two alternate realizations proposed in this paper is shown in Table I. It can be seen that the proposed 3-memristor realization requires the smallest number of memristors but requires 8 steps to compute, which is same as [8].…”

Section: (B) Steps Of Computationmentioning

confidence: 95%

“…In [8], a 2-to-1 multiplexer realization has been reported that requires six memristors as shown in Figure 4 4(c) respectively, which consists of seven implication steps and one data loading step. It may be noted that every implication step is of the form P = Q → P , that can be directly implemented using memristors.…”

Section: B Multiplexer Realization Using Memristorsmentioning

confidence: 99%

“…In a very recent paper [8] a memristor based design of 2-to-1 multiplexer (MUX) has been proposed that requires 6 memristor and 7 implication steps. However, it has been reported in [7] that for synthesizing any n-input Boolean function, n +2 memristors will suffice, with two work memristors.…”

Section: Introductionmentioning

confidence: 99%

“…However, it has been reported in [7] that for synthesizing any n-input Boolean function, n +2 memristors will suffice, with two work memristors. The present work aims to improve the design of [8] and try to attain/improve the bound reported in [7].…”

Section: Introductionmentioning

confidence: 99%

“…4. 6-memristor MUX implementation as in [8] In the following, we propose alternate 2-to-1 MUX implementations that require less number of memristors and/or computational steps.…”

mentioning

confidence: 99%

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BDD based synthesis of Boolean functions using memristors

Chakraborti

Chowdhary

Datta

et al. 2014

2014 9th International Design and Test Symposium (IDT)

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Very recently a new passive circuit element called memristor has been extensively investigated by researchers, which can be used for a variety of applications. This two-terminal device having few nanometer dimensions has been experimentally shown to possess both memory and resistor properties. This has also received great attention due to the fact that these devices can very easily be integrated on CMOS subsystems. Most of the logic design works in this context are based on material implication operation which can be very efficiently implemented using memristors. In this paper we propose an efficient realization of 2-to-1 multiplexer using memristors, and hence present a synthesis methodology that represents a given Boolean function as a Reduced Ordered Binary Decision Diagram (ROBDD) and then maps the same to memristor implementation. Index Terms-Memristor, logic design, multiplexer, binary decision diagram

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Section: (B) Steps Of Computationmentioning

confidence: 95%

Section: B Multiplexer Realization Using Memristorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…4. 6-memristor MUX implementation as in [8] In the following, we propose alternate 2-to-1 MUX implementations that require less number of memristors and/or computational steps.…”

mentioning

confidence: 99%

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BDD based synthesis of Boolean functions using memristors

Chakraborti

Chowdhary

Datta

et al. 2014

2014 9th International Design and Test Symposium (IDT)

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Artificial Skin Perception

et al. 2020

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interact with the world. This inspires scientists and engineers to develop flexible and stretchable electronic devices or systems to emulate the functionality of the human skin, known as electronic skin (e-skin). Recently, tremendous efforts have been made in the development of e-skin from materials innovation to structural designs, which concentrate on the improvement of sensing capability (i.e., stretchability, sensitivity, long-term monitoring, etc.), user-friendly detection mode (i.e., noninvasive, inflammation-free, gaspermeable, implantable, etc.), systemlevel integration (i.e., data transmission, power supply, etc.), and the realization of new functions (i.e., self-healing). [5][6][7] However, as one of the essential elements to mimic human intelligence, the functionality of perception, that is, interpretation of acquired sensory data, is still lacking in most e-skin systems. Implementing the perception functionality in a flexible and stretchable sensing system, referred to as artificial skin perception is vital to realizing an authentically intelligent artificial skin, with capabilities beyond human skin (Figure 1b). Coupled with sensing, feedback, and other technologies (Figure 1c), artificial skin perception will significantly accelerate the development of next-generation soft robotics, where a low-latency and energy-efficient data processing module is required to enable fast adaptation to dynamic environments.Currently, perception processes of most e-skin systems take place in centralized processing units, that is, computers or servers in the cloud, located far away from the sensing systems where sensory signals are generated. These sensory signals, usually time-serial, unstructured, and redundant, need to be continually sent to an external processing end. As a result, there will be a tremendous volume of data movements between the sensing end and the processing end, leading to huge energy consumption. [8,9] Moreover, the frequent and continual exchange of data causes a serious burden to data communication due to the limited bandwidth of communication channels in current sensing systems, especially when a large number of sensors are mounted on the large-area sensing system. [9][10][11] This leads to a notorious latency problem, known as a time delay in response for data communication. [9] The latency issue severely hinders the development of ultrafast responsive and delaysensitive intelligent systems, such as advanced robotics and Skin is the largest organ, with the functionalities of protection, regulation, and sensation. The emulation of human skin via flexible and stretchable electronics gives rise to electronic skin (e-skin), which has realized artificial sensation and other functions that cannot be achieved by conventional electronics. To date, tremendous progress has been made in data acquisition and transmission for e-skin systems, while the implementation of perception within systems, that is, sensory data processing, is still in its infancy. Integrating the perception functionality into a flex...

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Memristive Stateful Logic for Edge Boolean Computers

Kim

Son

Kim

2021

Advanced Intelligent Systems

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Memristive stateful logic enables complete in-memory computing, allowing low-power and low-cost Boolean computing. Its characteristics are consistent with the requirements of edge computing devices, which will occupy a considerable segment in the coming Internet of things (IoT) era. In this review, recent developments in stateful logic technology are intensively explored. The topics include a summary of the evolution of stateful logic gates and their cascading strategies for Boolean computing. Also, array-level data manipulation is discussed, and its role in realizing massive computation inside the memory. Finally, the logic operation error issue is discussed, with feasible solutions.

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A novel digital logic implementation approach on nanocrossbar arrays using memristor-based multiplexers

Cited by 42 publications

References 37 publications

BDD based synthesis of Boolean functions using memristors

BDD based synthesis of Boolean functions using memristors

Artificial Skin Perception

Memristive Stateful Logic for Edge Boolean Computers

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