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In recent times, AI and deep learning have witnessed explosive growth in almost every subject involving data. Complex data analyses problems that took prolonged periods, or required laborious manual effort, are now being tackled through AI and deep-learning techniques with unprecedented accuracy. Machine learning (ML) using Convolutional Neural Networks (CNNs) has shown great promise for such applications. However, traditional CPU-based sequential computing no longer can meet the requirements of mission-critical applications which are compute-intensive and require low latency. Heterogeneous computing (HGC), with CPUs integrated with accelerators such as GPUs and FPGAs, offers unique capabilities to accelerate CNNs. In this presentation, we will focus on using FPGA-based reconfigurable computing to accelerate various aspects of CNN. We will begin with the current state of the art in using FPGAs for CNN acceleration, followed by the related R&D activities (outlined below) in the SHREC* Center at the University of Florida, based on which we will discuss the opportunities in heterogeneous computing for machine learning.
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Applications of artificial intelligence technologies in water environments: From basic techniques to novel tiny machine learning systems
Artificial intelligence (AI) and machine learning (ML) are novel techniques to detect hidden patterns in environmental data. Despite their capabilities, these novel technologies have not been seriously used for real-world problems, such as real-time environmental monitoring. This survey established a framework to advance the novel applications of AI and ML techniques such as Tiny Machine Learning (TinyML) in water environments. The survey covered deep learning models and their advantages over classical ML models. The deep learning algorithms are the heart of TinyML models and are of paramount importance for practical uses in water environments. This survey highlighted the capabilities and discussed the possible applications of the TinyML models in water environments. This study indicated that the TinyML models on microcontrollers are useful for a number of cutting-edge problems in water environments, especially for monitoring purposes. The TinyML models on microcontrollers allow for in situ real-time environmental monitoring without transferring data to the cloud. It is concluded that monitoring systems based on TinyML models offer cheap tools to autonomously track pollutants in water and can replace traditional monitoring methods.
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- Award ID(s):
- 2300369
- PAR ID:
- 10512347
- Editor(s):
- Chen, Guohua; Khan, Faisal
- Publisher / Repository:
- Elservier
- Date Published:
- Journal Name:
- Process Safety and Environmental Protection
- Volume:
- 180
- ISSN:
- 0957-5820
- Page Range / eLocation ID:
- 10 to 22
- Subject(s) / Keyword(s):
- Artificial intelligence Deep learning TinyML Microcontrollers Monitoring
- Format(s):
- Medium: X Size: 1MB Other: pdf
- Size(s):
- 1MB
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.psep.2023.09.072
