New Processor Architecture and Its Use in Mobile Application Development

Fojtik, Rostislav

doi:10.1007/978-3-030-93677-8_48

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…The hardware used is a MacBook Air with the following technical specifications: 8 GB of RAM, an M1 chip including an 8-core CPU with four performance cores and four efficiency cores, a 7-core GPU, and a 16-core Neural Engine. The M1 CPU is reported to be 3.5 times faster than the previous Intel processor, with up to five times higher graphics processing capabilities [47]. Furthermore, when compared to using Google Collab with an accelerated CPU, in this study, the training time was eight times faster using the GPU, with an average of 36 s every epoch.…”

Section: Data Pre-processing Stagementioning

confidence: 66%

Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Syamsudin,

Chen,

Berutu

et al. 2024

Energies

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There is some risk of power quality disturbances at many stages of production, transformation, distribution, and energy consumption. The cornerstone for dealing with power quality problems is the characterization of power quality disturbances (PQDs). However, past research has focused on a narrow topic: noise disruption, overfitting, and training time. A new strategy is suggested to address this problem that combines efficient one-dimensional dataset compression with the convolutional neural network (CNN) classification algorithm. First, three types of compression algorithms: wavelet transform, autoencoder, and CNN, are proposed to be evaluated. According to the IEEE-1159 standard, the synthetic dataset was built with fourteen different PQD types. Furthermore, the PQD classification procedure integrated compressed data with the CNN classification algorithm. Finally, the suggested method demonstrates that combining CNN compression and classification methods can efficiently recognize PQDs. Even in noisy environments, PQD signal processing achieved up to 98.25% accuracy and managed the overfitting.

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Section: Data Pre-processing Stagementioning

confidence: 66%

Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Syamsudin,

Chen,

Berutu

et al. 2024

Energies

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“…The number of iterations decreased significantly due to the efficiency and performance of the dedicated neural engine processor. The neural engine processor was designed with ARM-v8-based technology, and this enabled executing each task with a single 64 bit instruction with up to 16 cores [29]. Furthermore, with the minimal energy consumption using the single-instruction technique, it enabled running all processor cores to execute tasks in parallel at the same time efficiently [30].…”

Section: Neural Engine Performancementioning

confidence: 99%

Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor

Awad

Hamad

2022

Electronics

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Clustering is one of the most significant applications in the big data field. However, using the clustering technique with big data requires an ample amount of processing power and resources due to the complexity and resulting increment in the clustering time. Therefore, many techniques have been implemented to improve the performance of the clustering algorithms, especially for k-means clustering. In this paper, the neural-processor-based k-means clustering technique is proposed to cluster big data by accumulating the advantage of dedicated machine learning processors of mobile devices. The solution was designed to be run with a single-instruction machine processor that exists in the mobile device’s processor. Running the k-means clustering in a distributed scheme run based on mobile machine learning efficiently can handle the big data clustering over the network. The results showed that using a neural engine processor on a mobile smartphone device can maximize the speed of the clustering algorithm, which shows an improvement in the performance of the cluttering up to two-times faster compared with traditional laptop/desktop processors. Furthermore, the number of iterations that are required to obtain (k) clusters was improved up to two-times faster than parallel and distributed k-means.

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“…The benchmarking of computer hardware is performed by using specialized software, designed to stress-test and to show the computational power limitations of the whole system or of its specific components, usually presenting the results in the form of a computation time [ 44 , 45 , 46 ]. It is very important that the tasks are as close to reality as possible, that is, that they correspond to real use cases in which the equipment will be used.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Usability of Apple M1 Processors for Various Machine Learning Tasks

Kasperek

Podpora

Kawala-Sterniuk

2022

Sensors

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In this paper, the authors have compared all of the currently available Apple MacBook Pro laptops, in terms of their usability for basic machine learning research applications (text-based, vision-based, tabular). The paper presents four tests/benchmarks, comparing four Apple Macbook Pro laptop versions: Intel based (i5) and three Apple based (M1, M1 Pro and M1 Max). A script in the Swift programming language was prepared, whose goal was to conduct the training and evaluation process for four machine learning (ML) models. It used the Create ML framework—Apple’s solution dedicated to ML model creation on macOS devices. The training and evaluation processes were performed three times. While running, the script performed measurements of their performance, including the time results. The results were compared with each other in tables, which allowed to compare and discuss the performance of individual devices and the benefits of the specificity of their hardware architectures.

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New Processor Architecture and Its Use in Mobile Application Development

Cited by 4 publications

References 5 publications

Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor

Comparison of the Usability of Apple M1 Processors for Various Machine Learning Tasks

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