Experimental Demonstration of Hopfield Neural Network using DNA molecules

2011 ◽  
Vol 1346 ◽  
Author(s):  
Hayri E. Akin ◽  
Dundar Karabay ◽  
Allen P. Mills ◽  
Cengiz S. Ozkan ◽  
Mihrimah Ozkan
Keyword(s):  
Neural Network ◽  
Data Storage ◽  
Large Scale ◽  
Fault Tolerant ◽  
Hamiltonian Path ◽  
Hopfield Neural Network ◽  
Dna Molecules ◽  
Processing Power ◽  
Dna Strands ◽  
Molecular Reactions

ABSTRACTDNA Computing is a rapidly-developing interdisciplinary area which could benefit from more experimental results to solve problems with the current biological tools. In this study, we have integrated microelectronics and molecular biology techniques for showing the feasibility of Hopfield Neural Network using DNA molecules. Adleman’s seminal paper in 1994 showed that DNA strands using specific molecular reactions can be used to solve the Hamiltonian Path Problem. This accomplishment opened the way for possibilities of massively parallel processing power, remarkable energy efficiency and compact data storage ability with DNA. However, in various studies, small departures from the ideal selectivity of DNA hybridization lead to significant undesired pairings of strands and that leads to difficulties in schemes for implementing large Boolean functions using DNA. Therefore, these error prone reactions in the Boolean architecture of the first DNA computers will benefit from fault tolerance or error correction methods and these methods would be essential for large scale applications. In this study, we demonstrate the operation of six dimensional Hopfield associative memory storing various memories as an archetype fault tolerant neural network implemented using DNA molecular reactions. The response of the network suggests that the protocols could be scaled to a network of significantly larger dimensions. In addition the results are read on a Silicon CMOS platform exploiting the semiconductor processing knowledge for fast and accurate hybridization rates.

Data Storage, Retrieval and Management

2010 ◽  
pp. 111-140
Author(s):  
Valentin Cristea ◽  
Ciprian Dobre ◽  
Corina Stratan ◽  
Florin Pop
Keyword(s):  
Data Storage ◽  
Resource Sharing ◽  
High Performance ◽  
Large Scale ◽  
Workflow Management ◽  
Large Data ◽  
Data Retrieval ◽  
Distributed Data Storage ◽  
Processing Power ◽  
Data Transfers

The latest advances in network and distributedsystem technologies now allow integration of a vast variety of services with almost unlimited processing power, using large amounts of data. Sharing of resources is often viewed as the key goal for distributed systems, and in this context the sharing of stored data appears as the most important aspect of distributed resource sharing. Scientific applications are the first to take advantage of such environments as the requirements of current and future high performance computing experiments are pressing, in terms of even higher volumes of issued data to be stored and managed. While these new environments reveal huge opportunities for large-scale distributed data storage and management, they also raise important technical challenges, which need to be addressed. The ability to support persistent storage of data on behalf of users, the consistent distribution of up-to-date data, the reliable replication of fast changing datasets or the efficient management of large data transfers are just some of these new challenges. In this chapter we discuss how the existing distributed computing infrastructure is adequate for supporting the required data storage and management functionalities. We highlight the issues raised from storing data over large distributed environments and discuss the recent research efforts dealing with challenges of data retrieval, replication and fast data transfers. Interaction of data management with other data sensitive, emerging technologies as the workflow management is also addressed.

scholarly journals Comparative Study between Big Data Analysis Techniques in Intrusion Detection

2018 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Mounir Hafsa ◽  
Farah Jemili
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Large Scale ◽  
Fault Tolerant ◽  
Time Frame ◽  
Cyber Attacks ◽  
Sensitive Information ◽  
Cyber Attack ◽  
Processing Power ◽  
Damage Costs

Cybersecurity ventures expect that cyber-attack damage costs will rise to $11.5 billion in 2019 and that a business will fall victim to a cyber-attack every 14 seconds. Notice here that the time frame for such an event is seconds. With petabytes of data generated each day, this is a challenging task for traditional intrusion detection systems (IDSs). Protecting sensitive information is a major concern for both businesses and governments. Therefore, the need for a real-time, large-scale and effective IDS is a must. In this work, we present a cloud-based, fault tolerant, scalable and distributed IDS that uses Apache Spark Structured Streaming and its Machine Learning library (MLlib) to detect intrusions in real-time. To demonstrate the efficacy and effectivity of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities. A decision tree algorithm is used to predict the nature of incoming data. For this task, the use of the MAWILab dataset as a data source will give better insights about the system capabilities against cyber-attacks. The experimental results showed a 99.95% accuracy and more than 55,175 events per second were processed by the proposed system on a small cluster.

scholarly journals Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Corentin Delacour ◽  
Aida Todri-Sanial
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Hebbian Learning ◽  
Hopfield Neural Network ◽  
Analytical Framework ◽  
Relaxation Oscillators ◽  
Oscillatory Neural Network ◽  
Network Functionality ◽  
Fully Connected ◽  
Neuromorphic Architecture

Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.

scholarly journals Hardware implementation of a fault-tolerant Hopfield Neural Network on FPGAs

2016 ◽  
Vol 171 ◽  
pp. 1606-1609 ◽  
Author(s):  
Juan Antonio Clemente ◽  
Wassim Mansour ◽  
Rafic Ayoubi ◽  
Felipe Serrano ◽  
Hortensia Mecha ◽  
...  
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Fault Tolerant ◽  
Hopfield Neural Network

scholarly journals On Models and Capacity Bounds for DNA-based Storage Channels

2021 ◽  
Author(s):  
Zihui Yan ◽  
Cong Liang
Keyword(s):  
Data Storage ◽  
Storage System ◽  
Synchronization Error ◽  
Dna Molecules ◽  
Capacity Limits ◽  
Dna Storage ◽  
Capacity Bounds ◽  
Dna Strands ◽  
Term Data

In recent years, DNA-based systems have become a promising medium for long-term data storage. There are two layers of errors in DNA-based storage systems. The first is the dropouts of the DNA strands, which has been characterized in the shuffling-sampling channel. The second is insertions, deletions, and substitutions of nucleotides in individual DNA molecules. In this paper, we describe a DNA noisy synchronization error channel to characterize the errors in individual DNA molecules. We derive non-trivial lower and upper capacity bounds of the DNA noisy synchronization error channel based on information theory. By cascading these two channels, we provide theoretical capacity limits of the DNA storage system. These results reaffirm that DNA is a reliable storage medium with high storage density potential.

scholarly journals Robust data storage in DNA by de Bruijn graph-based decoding

2021 ◽  
Author(s):  
Lifu Song ◽  
Feng Geng ◽  
Ziyi Song ◽  
Bing-Zhi Li ◽  
Ying-Jin Yuan
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Search Algorithm ◽  
De Bruijn Graph ◽  
Large Scale Data ◽  
Dna Strands ◽  
Pcr Products ◽  
Path Search ◽  
De Bruijn ◽  
Linear Decoding

Abstract Data storage in DNA, which store information in polymers, is a potential technology with high density and long-term features. However, the indels, strand rearrangements, and strand breaks that emerged during synthesis, amplification, sequencing, and storage of DNA molecules need to be handled. Here, we report a de Bruijn graph-based, greedy path search algorithm (DBG-GPS), which can efficiently handle all these issues by efficient reconstruction of the DNA strands. DBG-GPS achieves accurate data recovery with low-quality, deep error-prone PCR products, and accelerated aged DNA samples (solution, 70℃ for two weeks). The robustness of DBG-GPS was verified with 100 times of multiple retrievals using PCR products with massive unspecific amplifications. Moreover, DBG-GPS shows linear decoding complexity and more than 100 times faster than the multiple alignment-based methods, indicating a suitable solution for large-scale data storage.

scholarly journals An Efficient Interconnection System for Neural NOC Using Fault Tolerant Routing Method

2021 ◽  
Vol 2089 (1) ◽  
pp. 012069
Author(s):  
A. Pradeep kumar ◽  
Y. Devendar Reddy ◽  
T. Srinivas Reddy ◽  
K. Jamal
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Fault Tolerant ◽  
Interconnection Network ◽  
Heavy Traffic ◽  
Data Traffic ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
On Chip ◽  
Hardware Costs

Abstract Large scale Neural Network (NN) accelerators typically have multiple processing nodes that can be implemented as a multi-core chip, and can be organized on a network of chips (noise) corresponding to neurons with heavy traffic. Portions of several NoC-based NN chip-to-chip interconnect networks are linked to further enhance overall nerve amplification capacity. Large volumes of multicast on-chip or cross-chip can further complicate the construction of a cross-link network and create a NN barrier of device capacity and resources. In this paper, this refer to inter-chip and inter-chip communication strategies known as neuron connection for NN accelerators. Interconnect for powerful fault-tolerant routing system neural NoC is implemented in this paper. This recommends crossbar arbitration placement, virtual interrupts, and path-based parallelization strategies in terms of intra-chip communications for the virtual channel routing resulting in higher NoC output at lower hardware costs. A lightweight NoC compatible chip-to-chip interconnection scheme is proposed regarding to inter-chip communication for multicast-based data traffic to enable efficient interconnection for NoC-based NN chips. Moreover, the proposed methods will be tested with four Field Programmable Gate Arrays (FPGAs) on four hard-wired deep neural network (DNN) chips. From the experimental results it can be illustrate that a high throguput can obtained effectively by the proposed interconnection network in handling thedata traffic and low DNN through advanced links.

scholarly journals Cloud and Edge Computing for Developing Smart Factory Models using a iFogSim Wrapper: Transportation Management System (TMS) Case Study

2020 ◽  
Author(s):  
Dhairya Patel ◽  
Sabah Mohammed
Keyword(s):  
Data Storage ◽  
Management System ◽  
Large Scale ◽  
Edge Computing ◽  
Smart Factory ◽  
Transportation Management ◽  
Use Of Resources ◽  
Active User ◽  
Processing Power ◽  
Transportation Simulation

<p><b>The given literature focuses on developing a Smart Factory model based on Cloud and Edge computing used to develop Transportation Management System(TMS) using a iFogSim wrapper. Cloud computing identifies data centres for users and offer computer system services on-demand, including data storage and processing power, without direct active user management. In the smart industry, several devices are connected together across the internet, where vast volumes of data are collected during the entire process of output. Thus, to handle this data smart factory based on cloud and edge computing is used. The intelligent cloud-based factory offers some facility like large scale analysis of data. Concepts like fog and edge computing play a significant role in extending data storage and network capacities in the cloud that addresses some challenges, such as over-full bandwidth and latency. The literature also focuses on the implementation of TMS using the iFogSim Simulator. The simulator provides efficient execution of TMS by showing the amount of resources used which gives an idea regarding optimum use of resources. All types of data related to TMS is obtained at cloud by using smart factory like object location, time taken and energy consumption. To implement the TMS we have created a topology which displays various devices connected to the cloud which gives necessary information regarding the ongoing transportation simulation.</b></p>

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