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Download Dns Benchmark For Android
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Niccoletta Boyer
Jan 3, 2024, 1:27:43 AM1/3/24
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Since the Nitrogen8M leverages the new NXP i.MX8MQ processor we thought providing a benchmark would be useful for those unfamiliar with the new CPU features. For this exercise we settled on AnTuTu Benchmark on Android.
download dns benchmark for android
Download Zip https://t.co/l0wZ56MlaE
Use --run-abridged-story-set to run a shortened version of a benchmark with a representative subset of the stories included (Note that some benchmarks do not have an abridged version yet. Instructions for abridging a benchmark are here). Example:
Announced on November 13 2013, the Moto G is the lower-cost variant of the Moto X. Do not make the mistake of benchmarking the Moto G in comparison to the Moto X, because the Moto G is one of the firmest performing mid-range smartphone on the market. I'd go so far as labelling the Moto G as 'the handset to beat' in 2014 for many manufacturers looking to ship a handset in the sub $200 price range. It's that good.
Thanks to the work of Tom's Guide's testing team, we've got four different benchmarks for the iPhone 15 Pro to show you, along with results for last year's iPhone 14 Pro and for some of the top Android devices on the market right now. The exact winners and losers change depending on the test, but if there's one common theme, it's that the iPhone 15 Pro is way ahead of the competition.
For now, though, it's safe to say that an iPhone 15 Pro (possibly even more so than the larger iPhone 15 Pro Max) is the most powerful phone on the market. And even if its CPU and GPU benchmarks eventually get beaten, looking back at previous generations of iPhone suggest that machine learning and media encoding tasks will still be far faster than any Android-powered competitor for years to come.
Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark ( -benchmark.com) that are covering all main existing hardware configurations.
The rest of the paper is arranged as follows. In Sect. 2 we describe the hardware acceleration resources available on the main chipset platforms, as well as the programming interfaces for accessing them. Section 3 gives an overview of popular mobile deep learning frameworks. Section 4 provides a detailed description of the benchmark architecture, its programming implementation, and the computer vision tests that it includes. Section 5 shows the experimental results and inference times for different deep learning architectures, for various Android devices and chipsets. Section 6 analyzes the obtained results. Finally, Sect. 7 concludes the paper.
The current release of the AI Benchmark (2.0.0) is using the TensorFlow Lite [60] library as a backend for running all embedded deep learning models. Though the previous release was originally developed based on TF Mobile [61], its lack of NNAPI support imposed critical constraints on using hardware acceleration resources, and thus was later deprecated. The actual benchmark version was compiled with the latest TF Lite nightly build where some issues present in the stable TensorFlow versions were already solved.
The benchmark consists of nine deep learning tests described in the previous section. These can be generally divided into two groups. The first group includes tests 1, 2, 4, 5, 8, 9. Those use CNN models fully supported by NNAPI (i.e., all underlying TensorFlow operations are implemented in NNAPI introduced in Android 8.1), and therefore they can run with hardware acceleration on devices with appropriate chipsets and drivers. NNAPI is always enabled in these tests to avoid the situation when the system fails to automatically detect the presence of AI accelerators and performs all computations on CPU. It should also be mentioned that the first test runs a quantized CNN model and is used to check the performance of accelerated INT8-based computations.
In this section, we present quantitative benchmark results obtained from over 10,000 mobile devices tested in the wild. The scores of each device/SoC are presented in Tables 2 and 3 that are showing average processing time per one image for each test/network, maximum possible image resolution that can be processed by SRCNN model and the total aggregated AI score. The scores were calculated by averaging all obtained results of the corresponding devices/SoCs after removing the outliers. The description of the results is provided below.
The last topic that we want to address here is the use of quantized networks. Their current applicability is rather limited, as there are still no standard and reliable tools for quantizing networks trained even for image classification, not to mention more complex tasks. At the moment we can expect two different ways of development in this area. In the first case, the problem of quantization will be largely solved at some point, and the majority of neural networks deployed on smartphones will be quantized. In the second case, specific NPUs supporting float networks will become even more powerful and efficient, and the need for quantization will disappear as this happened to many optimized solutions developed due to the lack of computational power in the past. Since we cannot easily predict the future outcome, we will still be using a mixture of quantized and float models in the benchmark with predominance of the second ones, though in the future releases the corresponding ratio might be significantly altered.
Since currently there are still many important open questions that might be answered only with new major software and hardware releases related to machine learning frameworks and new dedicated chipsets, we are planning to publish regular benchmark reports describing the actual state of AI acceleration on mobile devices, as well as changes in the machine learning field, new efficient deep learning models developed for mobile [27], and the corresponding adjustments made in the benchmark to reflect them. The latest results obtained with the AI Benchmark and the description of the actual tests will also be updated monthly on the project website: -benchmark.com. Additionally, in case of any technical problems or some additional questions you can always contact the first two authors of this paper.
In this paper, we discussed the latest achievements in the area of machine learning and AI in the Android ecosystem. First, we presented an overview of all currently existing mobile chipsets that can be potentially used for accelerating the execution of neural networks on smartphones and other portable devices, and described popular mobile frameworks for running AI algorithms on mobile devices. We presented the AI Benchmark that measures different performance aspects associated with running deep neural networks on smartphones and other Android devices, and discussed the real-world results obtained with this benchmark from over 10,000 mobile devices and more than 50 different mobile SoCs. Finally, we discussed future perspectives of software and hardware development related to this area and gave our recommendations regarding the current deployment of deep learning models on Android devices.
Amlogic A311D2 offers a massive boost of 67% in 3D graphics performance against Amlogic A311D (aka S922X-B) in 3Dmark Sling Shot Extreme benchmark, thanks to an upgrade from an Arm Mali-G52 MP4 (6EE) to an Arm Mali-G52 MP8 (8EE) GPU, plus possibly a higher GPU frequency.
I tried to install Antutu 7.2.3 to get results comparable to what I got with Amlogic A311D and Rockchip RK3399, but sadly, there always seemed to be a mismatch between Antutu and Antutu 3Dbench versions, and the benchmark simply refused to run.
N2 - Ontology reasoning, in particular query answering with Description Logics-based ontologies, is a power-consuming task, especially in mobile settings where such a resource is limited and shared by other processes. To be able to determine whether a reasoning task will be consuming significant amount of power, a benchmark framework will be needed for ontology designers and reasoner developers. In this paper, we report our work on a power consumption benchmark framework that can be used to evaluate and compare the level of battery consumption of different Description Logics reasoners on Android devices. To calculate the power consumption of the reasoners, we measured the current flow and the voltage of the integrated battery. We focus on Android-based devices and evaluate the framework using 3 popular DL reasoners.
AB - Ontology reasoning, in particular query answering with Description Logics-based ontologies, is a power-consuming task, especially in mobile settings where such a resource is limited and shared by other processes. To be able to determine whether a reasoning task will be consuming significant amount of power, a benchmark framework will be needed for ontology designers and reasoner developers. In this paper, we report our work on a power consumption benchmark framework that can be used to evaluate and compare the level of battery consumption of different Description Logics reasoners on Android devices. To calculate the power consumption of the reasoners, we measured the current flow and the voltage of the integrated battery. We focus on Android-based devices and evaluate the framework using 3 popular DL reasoners.
35fe9a5643
download dns benchmark for android
Download Zip https://t.co/l0wZ56MlaE
Use --run-abridged-story-set to run a shortened version of a benchmark with a representative subset of the stories included (Note that some benchmarks do not have an abridged version yet. Instructions for abridging a benchmark are here). Example:
Announced on November 13 2013, the Moto G is the lower-cost variant of the Moto X. Do not make the mistake of benchmarking the Moto G in comparison to the Moto X, because the Moto G is one of the firmest performing mid-range smartphone on the market. I'd go so far as labelling the Moto G as 'the handset to beat' in 2014 for many manufacturers looking to ship a handset in the sub $200 price range. It's that good.
Thanks to the work of Tom's Guide's testing team, we've got four different benchmarks for the iPhone 15 Pro to show you, along with results for last year's iPhone 14 Pro and for some of the top Android devices on the market right now. The exact winners and losers change depending on the test, but if there's one common theme, it's that the iPhone 15 Pro is way ahead of the competition.
For now, though, it's safe to say that an iPhone 15 Pro (possibly even more so than the larger iPhone 15 Pro Max) is the most powerful phone on the market. And even if its CPU and GPU benchmarks eventually get beaten, looking back at previous generations of iPhone suggest that machine learning and media encoding tasks will still be far faster than any Android-powered competitor for years to come.
Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark ( -benchmark.com) that are covering all main existing hardware configurations.
The rest of the paper is arranged as follows. In Sect. 2 we describe the hardware acceleration resources available on the main chipset platforms, as well as the programming interfaces for accessing them. Section 3 gives an overview of popular mobile deep learning frameworks. Section 4 provides a detailed description of the benchmark architecture, its programming implementation, and the computer vision tests that it includes. Section 5 shows the experimental results and inference times for different deep learning architectures, for various Android devices and chipsets. Section 6 analyzes the obtained results. Finally, Sect. 7 concludes the paper.
The current release of the AI Benchmark (2.0.0) is using the TensorFlow Lite [60] library as a backend for running all embedded deep learning models. Though the previous release was originally developed based on TF Mobile [61], its lack of NNAPI support imposed critical constraints on using hardware acceleration resources, and thus was later deprecated. The actual benchmark version was compiled with the latest TF Lite nightly build where some issues present in the stable TensorFlow versions were already solved.
The benchmark consists of nine deep learning tests described in the previous section. These can be generally divided into two groups. The first group includes tests 1, 2, 4, 5, 8, 9. Those use CNN models fully supported by NNAPI (i.e., all underlying TensorFlow operations are implemented in NNAPI introduced in Android 8.1), and therefore they can run with hardware acceleration on devices with appropriate chipsets and drivers. NNAPI is always enabled in these tests to avoid the situation when the system fails to automatically detect the presence of AI accelerators and performs all computations on CPU. It should also be mentioned that the first test runs a quantized CNN model and is used to check the performance of accelerated INT8-based computations.
In this section, we present quantitative benchmark results obtained from over 10,000 mobile devices tested in the wild. The scores of each device/SoC are presented in Tables 2 and 3 that are showing average processing time per one image for each test/network, maximum possible image resolution that can be processed by SRCNN model and the total aggregated AI score. The scores were calculated by averaging all obtained results of the corresponding devices/SoCs after removing the outliers. The description of the results is provided below.
The last topic that we want to address here is the use of quantized networks. Their current applicability is rather limited, as there are still no standard and reliable tools for quantizing networks trained even for image classification, not to mention more complex tasks. At the moment we can expect two different ways of development in this area. In the first case, the problem of quantization will be largely solved at some point, and the majority of neural networks deployed on smartphones will be quantized. In the second case, specific NPUs supporting float networks will become even more powerful and efficient, and the need for quantization will disappear as this happened to many optimized solutions developed due to the lack of computational power in the past. Since we cannot easily predict the future outcome, we will still be using a mixture of quantized and float models in the benchmark with predominance of the second ones, though in the future releases the corresponding ratio might be significantly altered.
Since currently there are still many important open questions that might be answered only with new major software and hardware releases related to machine learning frameworks and new dedicated chipsets, we are planning to publish regular benchmark reports describing the actual state of AI acceleration on mobile devices, as well as changes in the machine learning field, new efficient deep learning models developed for mobile [27], and the corresponding adjustments made in the benchmark to reflect them. The latest results obtained with the AI Benchmark and the description of the actual tests will also be updated monthly on the project website: -benchmark.com. Additionally, in case of any technical problems or some additional questions you can always contact the first two authors of this paper.
In this paper, we discussed the latest achievements in the area of machine learning and AI in the Android ecosystem. First, we presented an overview of all currently existing mobile chipsets that can be potentially used for accelerating the execution of neural networks on smartphones and other portable devices, and described popular mobile frameworks for running AI algorithms on mobile devices. We presented the AI Benchmark that measures different performance aspects associated with running deep neural networks on smartphones and other Android devices, and discussed the real-world results obtained with this benchmark from over 10,000 mobile devices and more than 50 different mobile SoCs. Finally, we discussed future perspectives of software and hardware development related to this area and gave our recommendations regarding the current deployment of deep learning models on Android devices.
Amlogic A311D2 offers a massive boost of 67% in 3D graphics performance against Amlogic A311D (aka S922X-B) in 3Dmark Sling Shot Extreme benchmark, thanks to an upgrade from an Arm Mali-G52 MP4 (6EE) to an Arm Mali-G52 MP8 (8EE) GPU, plus possibly a higher GPU frequency.
I tried to install Antutu 7.2.3 to get results comparable to what I got with Amlogic A311D and Rockchip RK3399, but sadly, there always seemed to be a mismatch between Antutu and Antutu 3Dbench versions, and the benchmark simply refused to run.
N2 - Ontology reasoning, in particular query answering with Description Logics-based ontologies, is a power-consuming task, especially in mobile settings where such a resource is limited and shared by other processes. To be able to determine whether a reasoning task will be consuming significant amount of power, a benchmark framework will be needed for ontology designers and reasoner developers. In this paper, we report our work on a power consumption benchmark framework that can be used to evaluate and compare the level of battery consumption of different Description Logics reasoners on Android devices. To calculate the power consumption of the reasoners, we measured the current flow and the voltage of the integrated battery. We focus on Android-based devices and evaluate the framework using 3 popular DL reasoners.
AB - Ontology reasoning, in particular query answering with Description Logics-based ontologies, is a power-consuming task, especially in mobile settings where such a resource is limited and shared by other processes. To be able to determine whether a reasoning task will be consuming significant amount of power, a benchmark framework will be needed for ontology designers and reasoner developers. In this paper, we report our work on a power consumption benchmark framework that can be used to evaluate and compare the level of battery consumption of different Description Logics reasoners on Android devices. To calculate the power consumption of the reasoners, we measured the current flow and the voltage of the integrated battery. We focus on Android-based devices and evaluate the framework using 3 popular DL reasoners.
35fe9a5643
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