Efficient Hybrid CMOS/Memristor Implementation of Bidirectional Associative Memory Using Passive Weight Array

Majdabadi, Mahdiyar Molahasani; Shokouhi, Shahriar B.; Ko, Seok‐Bum

doi:10.1016/j.mejo.2020.104725

Cited by 6 publications

(3 citation statements)

References 60 publications

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“…The selection, crossover, mutation, and recombination of 1 and 2 are the same as the genetic algorithm. The detailed explanation of them can be found in the work of Deb [44]. max = ∑ ( → ) (30) 5) Construction of the spatio-temporal constraints through SSW and ocean surface current This study proved that there are bidirectional associations between U.prolifera green tides in the Yellow sea and Sina Weibo data.…”

Section: Inmentioning

confidence: 84%

“…( 14) and Eq. (15), when a given pattern is applied to BAM, the most similar stored pair of patterns will be received [44] Depending on the input pattern, BAM adjusts the outputs of neurons in X-layer and Y-layer to +1 or -1. Then, the neurons in X-layer and Y-layer are iteratively updated based on their weights and their previous values.…”

Section: ) Bidirectional Associative Memory Neural Network (Bam)mentioning

confidence: 99%

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Bidirectional Spatio-Temporal Association Between the Observed Results of Ulva Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo

Wang

Fang

Song

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Massive green tides caused by Ulva prolifera have annually occurred in the Yellow Sea since 2007, which has attracted much attention from the government and society. There are associations between the green tides in the Yellow Sea and social response in the social media (i.e. Sina Weibo), which are bidirectional and could be captured by the bidirectional neural network. For instance, how to detect daily U.prolifera green tides by fusing remote sensing data with social media data, and how to use the observed U.prolifera green tides to infer the social response are two challenges of State-Oceanic Administration, China. This paper first illustrated that there are bidirectional associations between green tides and Sina Weibo data. Then, this paper introduced a bidirectional spatiotemporal associative memory neural network model (BSAMNN) for modeling this bidirectional association from the spatiotemporal perspective. BSAMNN first extracted six characteristics from green tides and nine characteristics from the social responses in 2016-2019. Secondly, these characteristics were split by year and the characteristics in 2016-2018 were respectively put into the bidirectional associative memory neural network (BAM) which is a two-layer artificial neural network. Based on the BAM results and the observed data, the residual network was constructed. Thirdly, BSAMNN extracted the spatiotemporal rules from the characteristics in 2016-2018 as the constraints through the mining rule algorithm and modify the results via sea surface wind and ocean surface current. Last, BSAMNN put the characteristics in 2019 into BAM and used the residual network to modify the results, which was constrained by the spatiotemporal rules.The feasibility and reliability of our approach were demonstrated by using the U.prolifera green tides in 2019. The average accuracy, false alarm rate, and missing alarm rate of BSAMNN results were 0.69, 0.25, and 0.31 respectively which was 0.11 higher, 0.10 lower, and 0.11 lower than that of the traditional bidirectional associative memory neural network. The results indicated that our method is an effective alternative of linking the U.prolifera green tides and its public sentiments on social media.

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Section: Inmentioning

confidence: 84%

Section: ) Bidirectional Associative Memory Neural Network (Bam)mentioning

confidence: 99%

Bidirectional Spatio-Temporal Association Between the Observed Results of Ulva Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo

Wang

Fang

Song

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A two-terminal memristor is a viable option for hardware realization of the synaptic architecture in brain-inspired neuromorphic computation. The memristor is an excellent choice for forming neural synapses because of its high density, nonvolatility, and compatibility with CMOS [60]. Compared to synapses made up of numerous transistors, a single memristor can imitate the function of a synapse while taking up less chip space and power.…”

Section: Memristor-based Synapse For Neuromorphic Computationmentioning

confidence: 99%

Review of memristor based neuromorphic computation: opportunities, challenges and applications

2024

Eng. Res. Express

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The memristor is regarded as one of the promising possibilities for next-generationcomputing systems due to its small size, easy construction, and low power consumption.Memristor-based novel computing architectures have demonstratedconsiderable promise for replacing or enhancing traditional computing platformsthat encounter difficulties in the big-data era. Additionally, the striking resemblancebetween the mechanisms governing the programming of memristance andthe manipulation of synaptic weight at biological synapses may be used to createunique neuromorphic circuits that function according to biological principles.Nevertheless, getting memristor-based computing into practice presentsmany technological challenges. This paper reviews the potential for memristorresearch at the device, circuit, and system levels, mainly using memristors todemonstrate neuromorphic computation. Here, the common issues obstructingthe development and widespread use of memristor-based computing systems arealso carefully investigated. This study speculates on the prospective applicationsof memristors, which can potentially transform the field of electronics altogether.

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Braille–Latin conversion using memristive bidirectional associative memory neural network

Vaidyaraman

Thyagarajan

Shruthi

et al. 2022

J Ambient Intell Human Comput

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Efficient Hybrid CMOS/Memristor Implementation of Bidirectional Associative Memory Using Passive Weight Array

Cited by 6 publications

References 60 publications

Bidirectional Spatio-Temporal Association Between the Observed Results of Ulva Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo

Bidirectional Spatio-Temporal Association Between the Observed Results of Ulva Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo

Review of memristor based neuromorphic computation: opportunities, challenges and applications

Braille–Latin conversion using memristive bidirectional associative memory neural network

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