scholarly journals Enhancing Performance of Reservoir Computing System Based on Coupled MEMS Resonators

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2961
Author(s):  
Tianyi Zheng ◽  
Wuhao Yang ◽  
Jie Sun ◽  
Xingyin Xiong ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Computing System ◽  
System Level ◽  
Reservoir Computing ◽  
Data Set ◽  
Rc Structures ◽  
Computing Paradigm ◽  
Simulated Performance ◽  
Mems Resonators ◽  
Rc Structure ◽  
Digital Algorithms

Reservoir computing (RC) is an attractive paradigm of a recurrent neural network (RNN) architecture, owning to the ease of training and existing neuromorphic implementation. Its simulated performance matches other digital algorithms on a series of benchmarking tasks, such as prediction tasks and classification tasks. In this article, we propose a novel RC structure based on the coupled MEMS resonators with the enhanced dynamic richness to optimize the performance of the RC system both on the system level and data set level. Moreover, we first put forward that the dynamic richness of RC comprises linear dynamic richness and nonlinear dynamic richness, which can be enhanced by adding delayed feedbacks and nonlinear nodes, respectively. In order to set forth this point, we compare three typical RC structures, a single-nonlinearity RC structure with single-feedback, a single-nonlinearity RC structure with double-feedbacks, and the couple-nonlinearity RC structure with double-feedbacks. Specifically, four different tasks are enumerated to verify the performance of the three RC structures, and the results show the enhanced dynamic richness by adding delayed feedbacks and nonlinear nodes. These results prove that coupled MEMS resonators offer an interesting platform to implement a complex computing paradigm leveraging their rich dynamical features.

scholarly journals Nonvolatile Memristor-Based Reservoir Computing System with Reservoir Controllability

2021 ◽  
Author(s):  
Yoonho Jang ◽  
Ji Hun Kim ◽  
Gyung Seok Woo ◽  
Hyun Jae Lee ◽  
Woohyun Kim ◽  
...  
Keyword(s):  
Time Constant ◽  
Computing System ◽  
Sequential Data ◽  
Reservoir Computing ◽  
Data Set ◽  
Recent Advances ◽  
Limited Application ◽  
Application Fields

Abstract Recent advances in reservoir computing (RC) using memristors have made it possible to perform complicated timing-related recognition tasks using simple hardware. However, the fixed reservoir dynamics in previous studies have severely limited application fields. In this study, RC was implemented with a reservoir that consisted of a W/HfO2/TiN memristor (M), a capacitor (C), and a resistor (R), in which the reservoir dynamics could be arbitrarily controlled by changing their parameters. After the capability of the RC to identify the static MNIST data set was proven, the system was adopted to recognize the sequential data set [ultrasound (malignancy of lesions) and electrocardiogram (arrhythmia)] that had a significantly different time constant (107 vs. 1 s). The suggested RC system feasibly performed the tasks by simply varying the C and R, while the M remained unvaried. These functionalities demonstrate the high adaptability of the present RC system compared to the previous ones.

Integrated Quantitative Analysis of the Phosphoproteome and Transcriptome in Tamoxifen-resistant Breast Cancer*

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Breast Cancer ◽  
High Rate ◽  
System Level ◽  
Clinical Samples ◽  
Transcriptome Data ◽  
Data Set ◽  
Reporter Assays ◽  
Treated Breast ◽  
Mcf 7 ◽  
Resistant Cells

Quantitative phosphoproteome and transcriptome analysisof ligand-stimulated MCF-7 human breast cancer cells wasperformed to understand the mechanisms of tamoxifen resistanceat a system level. Phosphoproteome data revealed thatWT cells were more enriched with phospho-proteins thantamoxifen-resistant cells after stimulation with ligands.Surprisingly, decreased phosphorylation after ligand perturbationwas more common than increased phosphorylation.In particular, 17?-estradiol induced down-regulation inWT cells at a very high rate. 17?-Estradiol and the ErbBligand heregulin induced almost equal numbers of up-regulatedphospho-proteins in WT cells. Pathway and motifactivity analyses using transcriptome data additionallysuggested that deregulated activation of GSK3? (glycogensynthasekinase 3?) and MAPK1/3 signaling might be associatedwith altered activation of cAMP-responsive elementbindingprotein and AP-1 transcription factors intamoxifen-resistant cells, and this hypothesis was validatedby reporter assays. An examination of clinical samples revealedthat inhibitory phosphorylation of GSK3? at serine 9was significantly lower in tamoxifen-treated breast cancerpatients that eventually had relapses, implying that activationof GSK3? may be associated with the tamoxifen-resistantphenotype. Thus, the combined phosphoproteomeand transcriptome data set analyses revealed distinct signal

Hardware-Optimized Reservoir Computing System for Edge Intelligence Applications

2021 ◽  
Author(s):  
Alejandro Morán ◽  
Vincent Canals ◽  
Fabio Galan-Prado ◽  
Christian F. Frasser ◽  
Dhinakar Radhakrishnan ◽  
...  
Keyword(s):  
Computing System ◽  
Reservoir Computing

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

scholarly journals Neural Network Approach for Predicting Ship Speed and Fuel Consumption

2021 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Lúcia Moreira ◽  
Roberto Vettor ◽  
Carlos Guedes Soares
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Computing System ◽  
Fuel Oil ◽  
Network Computing ◽  
Peak Period ◽  
Neural Network Approach ◽  
Data Set ◽  
Navigation Data ◽  
Ship Speed

In this paper, simulations of a ship travelling on a given oceanic route were performed by a weather routing system to provide a large realistic navigation data set, which could represent a collection of data obtained on board a ship in operation. This data set was employed to train a neural network computing system in order to predict ship speed and fuel consumption. The model was trained using the Levenberg–Marquardt backpropagation scheme to establish the relation between the ship speed and the respective propulsion configuration for the existing sea conditions, i.e., the output torque of the main engine, the revolutions per minute of the propulsion shaft, the significant wave height, and the peak period of the waves, together with the relative angle of wave encounter. Additional results were obtained by also using the model to train the relationship between the same inputs used to determine the speed of the ship and the fuel consumption. A sensitivity analysis was performed to analyze the artificial neural network capability to forecast the ship speed and fuel oil consumption without information on the status of the engine (the revolutions per minute and torque) using as inputs only the information of the sea state. The results obtained with the neural network model show very good accuracy both in the prediction of the speed of the vessel and the fuel consumption.

scholarly journals Effects of Tension Reinforcement Ratio on Ductility of Mid-Rise Reinforced Concrete Structures

2018 ◽  
Vol 1 (1) ◽  
pp. 702-708
Author(s):  
Onur Onat ◽  
Burak Yön
Keyword(s):  
Reinforced Concrete ◽  
Failure Mode ◽  
Concrete Structures ◽  
Reinforcement Ratio ◽  
Reinforced Concrete Structures ◽  
Rc Structures ◽  
Beam Elements ◽  
First Case ◽  
Rc Structure ◽  
Determine Effect

Failure mode of reinforced concrete (RC) structures are classified according to tension reinforcement ratio of beam elements. To determine effect of tension reinforcement ratio on performance of RC structure, two planar RC structure were selected. One of them is 5 stories other of them is 7 stories. Two different concrete class, C20 and C25, were considered for analysis. Three tension reinforcement combinations were considered, three different tension reinforcement ratios were used. First case is the ratio of the tension reinforcement is lower than that of the compression reinforcement, second case is the ratio of the tension reinforcement is equal to the ratio of the compression reinforcement and third case is the ratio of the tensile reinforcement is higher than the compression reinforcement.

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