Neural network application for assessment of the asynchronous motors energy consumption

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

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Freely scalable and reconfigurable optical hardware for deep learning

Scientific Reports ◽

10.1038/s41598-021-82543-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Liane Bernstein ◽

Alexander Sludds ◽

Ryan Hamerly ◽

Vivienne Sze ◽

Joel Emer ◽

...

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Data Transfer ◽

Power Delivery ◽

Optical Data ◽

Optical Multicast ◽

Optical Neural Network ◽

Fully Connected ◽

Management Power

AbstractAs deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of $$>10\,\upmu $$ > 10 μ m.

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Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽

10.3390/pr9040655 ◽

2021 ◽

Vol 9 (4) ◽

pp. 655

Author(s):

Huanhuan Zhang ◽

Jigeng Li ◽

Mengna Hong

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Gradient Boosting ◽

Paper Machine ◽

Steam Consumption ◽

Tissue Paper ◽

Paper Machines ◽

Energy Consumption Model ◽

Extreme Gradient Boosting ◽

Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

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Prediction of energy consumption using artificial neural network method in one of shopping center in Cirebon city

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1098/4/042011 ◽

2021 ◽

Vol 1098 (4) ◽

pp. 042011

Author(s):

D H Pinanggih ◽

A G Abdullah ◽

D L Hakim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Energy Consumption ◽

Shopping Center ◽

Neural Network Method ◽

Network Method ◽

Artificial Neural Network Method ◽

Artificial Neural

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Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Electronics ◽

10.3390/electronics10080920 ◽

2021 ◽

Vol 10 (8) ◽

pp. 920

Author(s):

Liesle Caballero ◽

Álvaro Perafan ◽

Martha Rinaldy ◽

Winston Percybrooks

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Mobile Robot ◽

Energy Budget ◽

Dynamic Models ◽

Pearson Correlation ◽

Experimental Conditions ◽

Grid Map ◽

Proposed Model ◽

Exploration Task

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

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