1,960 research outputs found
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing
The memristive crossbar aims to implement analog weighted neural network,
however, the realistic implementation of such crossbar arrays is not possible
due to limited switching states of memristive devices. In this work, we propose
the design of an analog deep neural network with binary weight update through
backpropagation algorithm using binary state memristive devices. We show that
such networks can be successfully used for image processing task and has the
advantage of lower power consumption and small on-chip area in comparison with
digital counterparts. The proposed network was benchmarked for MNIST
handwritten digits recognition achieving an accuracy of approximately 90%
New acceleration technique for the backpropagation algorithm
Artificial neural networks have been studied for many years in the hope of achieving human like performance in the area of pattern recognition, speech synthesis and higher level of cognitive process. In the connectionist model there are several interconnected processing elements called the neurons that have limited processing capability. Even though the rate of information transmitted between these elements is limited, the complex interconnection and the cooperative interaction between these elements results in a vastly increased computing power; The neural network models are specified by an organized network topology of interconnected neurons. These networks have to be trained in order them to be used for a specific purpose. Backpropagation is one of the popular methods of training the neural networks. There has been a lot of improvement over the speed of convergence of standard backpropagation algorithm in the recent past. Herein we have presented a new technique for accelerating the existing backpropagation without modifying it. We have used the fourth order interpolation method for the dominant eigen values, by using these we change the slope of the activation function. And by doing so we increase the speed of convergence of the backpropagation algorithm; Our experiments have shown significant improvement in the convergence time for problems widely used in benchmarKing Three to ten fold decrease in convergence time is achieved. Convergence time decreases as the complexity of the problem increases. The technique adjusts the energy state of the system so as to escape from local minima
Estimating An Optimal Backpropagation Algorithm for Training An ANN with the EGFR Exon 19 Nucleotide Sequence: An Electronic Diagnostic Basis for Non–Small Cell Lung Cancer(NSCLC)
One of the most common forms of medical malpractices globally is an error in diagnosis. An improper
diagnosis occurs when a doctor fails to identify a disease or report a disease when the patient is actually
healthy. A disease that is commonly misdiagnosed is lung cancer. This cancer type is a major health problem
internationally because it is responsible for 15% of all cancer diagnosis and 29% of all cancer deaths. The two
major sub-types of lung cancer are; small cell lung cancer (about 13%) and non-small cell lung cancer
(%SCLC- about 87%). The chance of surviving lung cancer depends on its correct diagnosis and/or the stage at
the time it is diagnosed. However, recent studies have identified somatic mutations in the epidermal growth
factor receptor (EGFR) gene in a subset of non-small cell lung cancer (%SCLC) tumors. These mutations occur
in the tyrosine kinase domain of the gene. The most predominant of the mutations in all %SCLC patients
examined is deletion mutation in exon 19 and it accounts for approximately 90% of the EGFR-activating
mutations. This makes EGFR genomic sequence a good candidate for implementing an electronic diagnostic
system for %SCLC. In this study aimed at estimating an optimum backpropagation training algorithm for a
genomic based A%% system for %SCLC diagnosis, the nucleotide sequences of EGFR’s exon 19 of a noncancerous
cell were used to train an artificial neural network (A%%). Several A%% back propagation training
algorithms were tested in MATLAB R2008a to obtain an optimal algorithm for training the network. Of the nine
different algorithms tested, we achieved the best performance (i.e. the least mean square error) with the
minimum epoch (training iterations) and training time using the Levenberg-Marquardt algorithm
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