An Energy-Efficient Programmable Mixed-Signal Accelerator for Machine Learning Algorithms

Kang, Mingu; Srivastava, Prakalp; Adve, Vikram; Kim, Nam Sung; Shanbhag, Naresh R.

doi:10.1109/mm.2019.2929502

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Most of these courses are focusing on AI-inspired architecture and system education, which have received great attention and good effects. Prof. Naresh R. Shanbhag in UIUC This course aims to teach students how to design machine learning algorithms in chips directly, which mainly focuses on circuit and system implementations [8]. Prof. Vivienne Sze and Joel Emer in MIT offer a course named Hardware Architecture for Deep Learning since Fall 2017.…”

Section: Background and Related Coursesmentioning

confidence: 99%

Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures

Yang

Gao

Zhao

2020

Proceedings of the 2020 on Great Lakes Symposium on VLSI

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With the rapid development of artificial intelligence (AI) community, education in AI is receiving more and more attentions. There have been many AI related courses in the respects of algorithms and applications, while not many courses in system level are seriously taken into considerations. In order to bridge the gap between AI and computing systems, we are trying to explore how to conduct AI education from the perspective of computing systems. In this paper, a course practice in intelligent computing architectures are provided to demonstrate the system education in AI era. The motivation for this course practice is first introduced as well as the learning orientations. The main goal of this course aims to teach students for designing AI accelerators on FPGA platforms. The elaborated course contents include lecture notes and related technical materials. Especially several practical labs and projects are detailed illustrated. Finally, some teaching experiences and effects are discussed as well as some potential improvements in the future. CCS CONCEPTS• Computer systems organization → Neural networks; • Hardware → Application specific integrated circuits; Hardware accelerators.

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Section: Background and Related Coursesmentioning

confidence: 99%

Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures

Yang

Gao

Zhao

2020

Proceedings of the 2020 on Great Lakes Symposium on VLSI

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“…However, the decision tree classification method can process the sample set containing both discrete attributes and continuous attributes, and it is easier to convert with classification rules. It can also solve the over-fitting problem well through the pruning process, which is easy to apply to practical work [14][15][16][17][18]. Therefore, this study chooses the decision tree classification method to study the relationship between data and faults in the monitoring system.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning

Jiang

et al. 2020

Applied Sciences

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The performance of the brake system is directly related to the safety and reliability of the mine hoist operation. Mining the useful fault information in the operation of a mine hoist brake system, analyzing the abnormal parts and causes of the equipment, and making accurate early prediction and diagnosis of hidden faults are of great significance to ensure the safe and stable operation of a mine hoist. This study presents a fault diagnosis method for hoist disc brake system based on machine learning. First, the monitoring system collects the information of the hoist brake system, extracts the fault features, and pretreats it by SPSS (Statistical Product and Service Solutions). This work provides data support for fault classification. Then, due to the complex structure of the hoist brake system, the relationship between the fault factors often has a significant impact on the fault. Considering the correlation between the fault samples and the attributes of each sample data, the C4.5 decision tree algorithm is improved by adding Kendall concordance coefficient, and the improved algorithm is used to train the sample data to get the decision tree classification model. Finally, the fault sample of the hoist brake system is trained to get the algorithm model, and then the fault diagnosis rules are generated. The state of the brake system is judged by classifying the data. Experiments show that the improved C4.5 decision tree algorithm takes the relativity of conditional attributes into account, has a higher diagnostic accuracy when processing more data, and has concise and clear fault classification rules, which can meet the needs of hoist fault diagnosis.

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Comprehending In-memory Computing Trends via Proper Benchmarking

Shanbhag

Roy

2022

2022 IEEE Custom Integrated Circuits Conference (CICC)

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An Energy-Efficient Programmable Mixed-Signal Accelerator for Machine Learning Algorithms

Cited by 4 publications

References 4 publications

Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures

Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures

A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning

Comprehending In-memory Computing Trends via Proper Benchmarking

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