scholarly journals SLO-Aware Inference Scheduler for Heterogeneous Processors in Edge Platforms

2021 ◽  
Vol 18 (4) ◽  
pp. 1-26
Author(s):  
Wonik Seo ◽  
Sanghoon Cha ◽  
Yeonjae Kim ◽  
Jaehyuk Huh ◽  
Jongse Park
Keyword(s):  
Machine Learning ◽  
Data Center ◽  
Heterogeneous Computing ◽  
Service Level ◽  
Sensor Data ◽  
Scheduling Problem ◽  
Learning Models ◽  
Heterogeneous Processors ◽  
Scheduling Policies ◽  
Machine Learning Models

With the proliferation of applications with machine learning (ML), the importance of edge platforms has been growing to process streaming sensor, data locally without resorting to remote servers. Such edge platforms are commonly equipped with heterogeneous computing processors such as GPU, DSP, and other accelerators, but their computational and energy budget are severely constrained compared to the data center servers. However, as an edge platform must perform the processing of multiple machine learning models concurrently for multimodal sensor data, its scheduling problem poses a new challenge to map heterogeneous machine learning computation to heterogeneous computing processors. Furthermore, processing of each input must provide a certain level of bounded response latency, making the scheduling decision critical for the edge platform. This article proposes a set of new heterogeneity-aware ML inference scheduling policies for edge platforms. Based on the regularity of computation in common ML tasks, the scheduler uses the pre-profiled behavior of each ML model and routes requests to the most appropriate processors. It also aims to satisfy the service-level objective (SLO) requirement while reducing the energy consumption for each request. For such SLO supports, the challenge of ML computation on GPUs and DSP is its inflexible preemption capability. To avoid the delay caused by a long task, the proposed scheduler decomposes a large ML task to sub-tasks by its layer in the DNN model.

scholarly journals Practical Considerations for Accuracy Evaluation in Sensor-Based Machine Learning and Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3491 ◽  
Author(s):  
Issam Hammad ◽  
Kamal El-Sankary
Keyword(s):  
Machine Learning ◽  
Thermal Noise ◽  
Error Resilience ◽  
Sensor Data ◽  
Accuracy Evaluation ◽  
Sensor Failure ◽  
Learning Models ◽  
Analog To Digital ◽  
The Impact ◽  
Machine Learning Models

Accuracy evaluation in machine learning is based on the split of data into a training set and a test set. This critical step is applied to develop machine learning models including models based on sensor data. For sensor-based problems, comparing the accuracy of machine learning models using the train/test split provides only a baseline comparison in ideal situations. Such comparisons won’t consider practical production problems that can impact the inference accuracy such as the sensors’ thermal noise, performance with lower inference quantization, and tolerance to sensor failure. Therefore, this paper proposes a set of practical tests that can be applied when comparing the accuracy of machine learning models for sensor-based problems. First, the impact of the sensors’ thermal noise on the models’ inference accuracy was simulated. Machine learning algorithms have different levels of error resilience to thermal noise, as will be presented. Second, the models’ accuracy using lower inference quantization was compared. Lowering inference quantization leads to lowering the analog-to-digital converter (ADC) resolution which is cost-effective in embedded designs. Moreover, in custom designs, analog-to-digital converters’ (ADCs) effective number of bits (ENOB) is usually lower than the ideal number of bits due to various design factors. Therefore, it is practical to compare models’ accuracy using lower inference quantization. Third, the models’ accuracy tolerance to sensor failure was evaluated and compared. For this study, University of California Irvine (UCI) ‘Daily and Sports Activities’ dataset was used to present these practical tests and their impact on model selection.

scholarly journals A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server’s CPU Usage Prediction

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 67
Author(s):  
Qazi Zia Ullah ◽  
Gul Muhammad Khan ◽  
Shahzad Hassan ◽  
Asif Iqbal ◽  
Farman Ullah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Resource Utilization ◽  
Data Center ◽  
Data Centers ◽  
Electrical Energy ◽  
Learning Models ◽  
Cartesian Genetic Programming ◽  
Cloud Data ◽  
Machine Learning Models

Cloud computing use is exponentially increasing with the advent of industrial revolution 4.0 technologies such as the Internet of Things, artificial intelligence, and digital transformations. These technologies require cloud data centers to process massive volumes of workloads. As a result, the data centers consume gigantic amounts of electrical energy, and a large portion of data center electrical energy comes from fossil fuels. It causes greenhouse gas emissions and thus ensuing in global warming. An adaptive resource utilization mechanism of cloud data center resources is vital to get by with this huge problem. The adaptive system will estimate the resource utilization and then adjust the resources accordingly. Cloud resource utilization estimation is a two-fold challenging task. First, the cloud workloads are sundry, and second, clients’ requests are uneven. In the literature, several machine learning models have estimated cloud resources, of which artificial neural networks (ANNs) have shown better performance. Conventional ANNs have a fixed topology and allow only to train their weights either by back-propagation or neuroevolution such as a genetic algorithm. In this paper, we propose Cartesian genetic programming (CGP) neural network (CGPNN). The CGPNN enhances the performance of conventional ANN by allowing training of both its parameters and topology, and it uses a built-in sliding window. We have trained CGPNN with parallel neuroevolution that searches for global optimum through numerous directions. The resource utilization traces of the Bitbrains data center is used for validation of the proposed CGPNN and compared results with machine learning models from the literature on the same data set. The proposed method has outstripped the machine learning models from the literature and resulted in 97% prediction accuracy.

Generating Phone-quality Records to Train Machine Learning Models for Smartphone-based Earthquake Early Warning

2020 ◽  
Author(s):  
Zengwei Zheng ◽  
Lifei Shi ◽  
Sha Zhao ◽  
Jianmin Hou ◽  
Lin Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Transformation Method ◽  
Earthquake Early Warning ◽  
Sensor Data ◽  
Superior Performance ◽  
Quality Data ◽  
Learning Models ◽  
Seismic Networks ◽  
Machine Learning Models

Abstract Earthquake Early Warning (EEW) system detects earthquakes and sends an early warning to areas likely to be affected, which plays a significant role in reducing earthquake damage. In recent years, as with the widespread distribution of smartphones, as well as their powerful computing ability and advanced built-in sensors, a new interdisciplinary research method of smartphone-based earthquake early warning has emerged. Smartphones-based earthquake early warning system applies signal processing techniques and machine learning algorithms to the sensor data recorded by smartphones for better monitoring earthquakes. But it is challenging to collect abundant phone-recorded seismic data for training related machine learning models and selecting appropriate features for these models. One alternative way to solve this problem is to transform the data recorded by seismic networks into phone-quality data. In this paper, we propose such a transformation method by learning the differences between the data recorded by seismic networks and smartphones, in two scenarios: phone fixed and free located on tables, respectively. By doing this, we can easily generate abundant phone-quality earthquake data to train machine learning models used in EEW systems. We evaluate our transformation method by conducting various experiments, and our method performs much better than existing methods. Furthermore, we set up a case study where we use the transformed records to train machine learning models for earthquake intensity prediction. The results show that the model trained by using our transformed data produces superior performance, suggesting that our transformation method is useful for smartphone-based earthquake early warning.

scholarly journals An Exploration of Machine-Learning Estimation of Ground Reaction Force from Wearable Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 740
Author(s):  
Danica Hendry ◽  
Ryan Leadbetter ◽  
Kristoffer McKee ◽  
Luke Hopper ◽  
Catherine Wild ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Sensor Data ◽  
Wearable Sensor ◽  
Learning Models ◽  
Data Set ◽  
Sensor Model ◽  
Single Sensor ◽  
Machine Learning Models

This study aimed to develop a wearable sensor system, using machine-learning models, capable of accurately estimating peak ground reaction force (GRF) during ballet jumps in the field. Female dancers (n = 30) performed a series of bilateral and unilateral ballet jumps. Dancers wore six ActiGraph Link wearable sensors (100 Hz). Data were collected simultaneously from two AMTI force platforms and synchronised with the ActiGraph data. Due to sensor hardware malfunctions and synchronisation issues, a multistage approach to model development, using a reduced data set, was taken. Using data from the 14 dancers with complete multi-sensor synchronised data, the best single sensor was determined. Subsequently, the best single sensor model was refined and validated using all available data for that sensor (23 dancers). Root mean square error (RMSE) in body weight (BW) and correlation coefficients (r) were used to assess the GRF profile, and Bland–Altman plots were used to assess model peak GRF accuracy. The model based on sacrum data was the most accurate single sensor model (unilateral landings: RMSE = 0.24 BW, r = 0.95; bilateral landings: RMSE = 0.21 BW, r = 0.98) with the refined model still showing good accuracy (unilateral: RMSE = 0.42 BW, r = 0.80; bilateral: RMSE = 0.39 BW, r = 0.92). Machine-learning models applied to wearable sensor data can provide a field-based system for GRF estimation during ballet jumps.

scholarly journals A data-oriented approach for outlier detection

2019 ◽  
Vol 7 (01) ◽  
Author(s):  
Nripesh Trivedi
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Data Stream ◽  
Sensor Data ◽  
Learning Models ◽  
Hoeffding Tree ◽  
Tree Algorithms ◽  
Oriented Approach ◽  
Machine Learning Models

In this paper, characteristics of data obtained from the sensors (used in OpenSense project) are identified in order to build a data-oriented approach. This approach consists of application of Class Outliers: Distance Based (CODB) and Hoeffding tree algorithms. Subsequently, machine learning models were built to detect outliers in a sensor data stream. The approach presented in this paper may be used for developing methodologies for data-oriented outlier detection

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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