RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition

Jiang, Yuning; Kang, Jinfeng; Wang, Xinan

doi:10.1038/srep45233

Cited by 29 publications

(16 citation statements)

References 25 publications

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“…The SVM tried to find a hyperplane to separate the members from nonmembers of a particular GO family by maximizing the margin defined in protein feature space [ 92 ]. The KNN predicted the class of a protein by the majority vote of its neighbors with a given distance metric [ 93 ], and the PNN was a neural network based on Bayesian decision theory [ 94 ]. These three methods were directly applied under the Python environment.…”

Section: Methodsmentioning

confidence: 99%

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Hong

Luo

Zhang

et al. 2019

Briefings in Bioinformatics

125

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Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

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Section: Methodsmentioning

confidence: 99%

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Hong

Luo

Zhang

et al. 2019

Briefings in Bioinformatics

125

View full text Add to dashboard Cite

show abstract

“…This is because the synapses in HTM are binary in nature, i.e., they exhibit the same properties if they are above the permanence threshold regardless of the synapse's growth level and vice versa. In 2017, Jiang et al proposed a memristor device to implement the k-nearest neighbour algorithm and that exhibits properties required for HTM [40]. Fig.…”

Section: Experimental Methodologymentioning

confidence: 99%

“…In such scenario, these cells need to have their synaptic strength reduced to lower the likelihood of incorrect prediction (as in lines 19-23). After evaluating the cells' segments, their synaptic connections are updated, which occurs during the learning phase (lines [38][39][40][41][42][43][44][45][46][47].…”

Section: Temporal Memorymentioning

confidence: 99%

Neuromorphic System for Spatial and Temporal Information Processing

Zyarah

Gómez

Kudithipudi

2020

IEEE Trans. Comput.

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Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this article, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algorithmically, the system is based on hierarchical temporal memory that inherently offers online learning, resiliency, and fault tolerance. Architecturally, it is a full custom mixed-signal design with an underlying digital communication scheme and analog computational modules. Therefore, the proposed system features reconfigurability, real-time processing, low power consumption, and low-latency processing. The proposed architecture is benchmarked to predict on real-world streaming data. The network's mean absolute percentage error on the mixed-signal system is 1.129 X lower compared to its baseline algorithm model. This reduction can be attributed to device non-idealities and probabilistic formation of synaptic connections. We demonstrate that the combined effect of Hebbian learning and network sparsity also plays a major role in extending the overall network lifespan. We also illustrate that the system offers 3.46 X reduction in latency and 77.02 X reduction in power consumption when compared to a custom CMOS digital design implemented at the same technology node. By employing specific low power techniques, such as clock gating, we observe 161.37 X reduction in power consumption.

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“…While the difference of neighbor pixels is performed with the application of the corresponding voltage pulses on word, WL, and bit, BL lines, the inmemory processing across cross-point cells is performed only in ( + )/2 cycles where the crossbar dimension is × ∈ 2 . This is equivalent to the write/read, row by row in classical RRAM crossbar designs [29]. At each cycle, only one cross-point cell per line and per column is selected.…”

Section: B Rram-based In Memory Computing Architecturementioning

confidence: 99%

RRAM Crossbar-Based In-Memory Computation of Anisotropic Filters for Image Preprocessingloa

et al. 2020

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Anisotropic-diffusion is a commonly used signal preprocessing technique that allows extracting meaningful local characteristics from a signal, such as edges in an image and can be used to support higher-level processing tasks, such as shape detection. This paper presents a fully scalable CMOS-RRAM architecture of an edge-aware-anisotropic filtering algorithm aimed at computer vision applications. The CMOS circuitry controls the scale-space image data to perform pseudo-parallel in-memory computing and nonlinear processing through RRAM crossbar. The arithmetic operations for in-memory computation of brightness gradients are efficiently accumulated to produce the enhanced image in several iterations. The proposed architecture uses single RRAM as a computing and storage element to perform both arithmetic operations and accumulations. Thanks to the in-memory computation, memory accesses and arithmetic operations are reduced by 64% and 92%, respectively, compared to traditional digital implementations. This, in turn, results in a potential reduction of power and area costs of about 75% and 85%, respectively. The processing time is also reduced by 67%.

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RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition

Cited by 29 publications

References 25 publications

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Neuromorphic System for Spatial and Temporal Information Processing

RRAM Crossbar-Based In-Memory Computation of Anisotropic Filters for Image Preprocessingloa

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