AIoT Bench: Towards Comprehensive Benchmarking Mobile and Embedded Device Intelligence

Luo, Chunjie; Zhang, Fan; Huang, Cheng; Xiong, Xingwang; Chen, Jianan; Wang, Lei; Gao, Wanling; Ye, Hainan; Wu, Tong; Zhou, Runsong; Zhan, Jianfeng

doi:10.1007/978-3-030-32813-9_4

Cited by 49 publications

(27 citation statements)

References 7 publications

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“…By contrast, macro benchmarks refer to benchmarks that capture application-specific system performance metrics for different application domains. Only two benchmarks capture both micro and macro benchmarks, namely RIoTBench (64) and AIoTBench (65). The majority of benchmarks are macro benchmarks and only two utilize generic workloads, namely EdgeBench (66) and DeFog (10).…”

Section: Edge Performance Benchmarkingmentioning

confidence: 99%

“…Devices that run machine learning workloads: The benchmarking of machine-learning-specific workloads for various devices was presented in (65). Both micro benchmarks, such as the individual layers of a neural network, and macro benchmarks, such as applications in image classification, speech recognition, and language translation on the TensorFlow and Caffe2 frameworks, were considered.…”

Section: B1 Destinationmentioning

confidence: 99%

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A Survey on Edge Performance Benchmarking

et al. 2021

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Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores to provide more responsive services. Therefore, it is envisioned that a large-scale, geographically dispersed, and resource-rich distributed system will emerge and play a key role in the future Internet. However, given the loosely coupled nature of such complex systems, their operational conditions are expected to change significantly over time. In this context, the performance characteristics of such systems will need to be captured rapidly, which is referred to as performance benchmarking, for application deployment, resource orchestration, and adaptive decision-making. Edge performance benchmarking is a nascent research avenue that has started gaining momentum over the past five years. This article first reviews articles published over the past three decades to trace the history of performance benchmarking from tightly coupled to loosely coupled systems. It then systematically classifies previous research to identify the system under test, techniques analyzed, and benchmark runtime in edge performance benchmarking.

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Section: Edge Performance Benchmarkingmentioning

confidence: 99%

Section: B1 Destinationmentioning

confidence: 99%

A Survey on Edge Performance Benchmarking

et al. 2021

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“…Many of their benchmark suites, namely, AIBench, HPC AI500, AIoT Bench, Edge AIBench and BigDataBench, although not primarily focused on scientific applications, could be a useful complement to the SciML benchmarks proposed here. Each of these benchmark suites targets different domains of the problem, such as IoTs or Edge computing devices, and includes many different types of benchmarks covering micro kernels, components and applications [63][64][65][66][67]. -The CORAL-2 suite includes a ML/DL micro-benchmark suite that captures operations that are fundamental to deep learning and machine learning [60].…”

Section: Big Scientific Data and Machine Learning Benchmarks (A) Introductionmentioning

confidence: 99%

Machine learning and big scientific data

Hey

Butler

Jackson

et al. 2020

Phil. Trans. R. Soc. A.

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This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory site at Harwell near Oxford. Such 'Big Scientific Data' comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility, and the UK's Central Laser Facility. Increasingly, scientists are now needing to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and also to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now also used deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, they have been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems?After a brief review of some initial applications of machine learning at the Rutherford Appleton Laboratory, we focus on challenges and opportunities for AI in advancing materials science.Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from a number of different scientific domains. We conclude with some initial examples of our 'SciML' benchmark suite and of the research challenges these benchmarks will enable. Rosetta protein folding program at the University of Washington in Seattle [8], commented that 'DeepMind's scientists built on two algorithm strategies pioneered by others. First, by comparing vast troves of genomic data on other proteins, AlphaFold was able to better decipher which pairs of amino acids were most likely to wind up close to one another in folded proteins. Second, related comparisons also helped them gauge the most probable distance between neighboring pairs of amino acids and the angles at which they bound to their neighbors. Both approaches do better with the more data they evaluate, which makes them more apt to benefit from machine learning computer algorithms, such as AlphaFold, that solve problems by crunching large data sets' [9]. The predictions of the AlphaFold system were remarkably good and better on average than the other 97 competitors. However, there is still hope for scientists. After the competition David Baker remarked that 'Deep Mind made much better fold level predictions than everybody, including us, using DL on co-evolution data. For problems where there are not many homologous sequences, and for protein structure refinement, I would expect their approach to work less well, as it doesn't have any physical chemistry (they used Rosetta to build their final models from predicted distances)' [10].In this paper, we make some initial explorations into the application of such Deep Learning approaches ap...

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“…Coordinated by BenchCouncil (http://www.benchcouncil.org), we are also building an edge computing testbed with a federated learning framework to resolve security and privacy issue, which can be accessed from http: //www.benchcouncil.org/testbed/index.php. BenchCouncil also release dat-acenter AI benchmarks [8], HPC AI benchmarks [9], and IoT AI benchmarks [10], publicly available from http://www.benchcouncil.org/AIBench/index.html.…”

Section: Introductionmentioning

confidence: 99%

Edge AIBench: Towards Comprehensive End-to-End Edge Computing Benchmarking

Hao

Huang

et al. 2019

Lecture Notes in Computer Science

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In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues. So for edge computing benchmarking, we must take an end-to-end view, considering all three layers: client-side devices, edge computing layer, and cloud servers. Unfortunately, the previous work ignores this most important point. This paper presents the BenchCouncil's coordinated effort on edge AI benchmarks, named Edge AIBench. In total, Edge AIBench models four typical application scenarios: ICU Patient Monitor, Surveillance Camera, Smart Home, and Autonomous Vehicle with the focus on data distribution and workload collaboration on three layers. Edge AIBench is a part of the open-source AIBench project, publicly available from http: //www.benchcouncil.org/AIBench/index.html. We also build an edge computing testbed with a federated learning framework to resolve performance, privacy, and security issues.

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AIoT Bench: Towards Comprehensive Benchmarking Mobile and Embedded Device Intelligence

Cited by 49 publications

References 7 publications

A Survey on Edge Performance Benchmarking

A Survey on Edge Performance Benchmarking

Machine learning and big scientific data

Edge AIBench: Towards Comprehensive End-to-End Edge Computing Benchmarking

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