On-device Online Learning and Semantic Management of TinyML Systems

Haoyu Ren, Xue Li, Darko Anicic, Thomas A. Runkler·May 13, 2024

Summary

This paper investigates challenges and solutions for implementing TinyML systems in production, focusing on on-device online learning, federated meta-learning, and semantic management. TinyOL enables real-time adaptation to dynamic environments, while TinyReptile and TinyMetaFed enhance model generalization and communication efficiency across heterogeneous devices with limited labeled data. SeLoC-ML is introduced as a Semantic Web-based system for efficient model-device management at scale. The research showcases improvements in accuracy, resource efficiency, and engineering effort through real-world applications, addressing the need for comprehensive strategies in TinyML development. The work highlights the growing importance of TinyML in IoT devices and the need for further research in optimization, robustness, and integration into industrial settings.

Key findings

12

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address three critical challenges in developing TinyML systems in industries, focusing on efficient adaptation to changing conditions on single devices, handling deployment heterogeneity across multiple devices, and managing TinyML resources effectively over time . While these challenges are not entirely new, the paper proposes innovative approaches based on prior research to tackle these problems in the context of TinyML systems .


What scientific hypothesis does this paper seek to validate?

The scientific hypothesis that this paper aims to validate is related to on-device online learning and semantic management of TinyML systems. The paper focuses on exploring the integration of semantic web technology to handle diverse data sources in TinyML, aiming to enhance interoperability between IoT devices and ML models . The study delves into the development of a semantic ontology tailored for neural network models in IoT scenarios, considering hardware specifications like resource and platform requirements . The research seeks to bridge the gap between IoT devices and ML models by providing a common representation, thereby improving the ecosystem's interoperability .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "On-device Online Learning and Semantic Management of TinyML Systems" proposes several innovative ideas, methods, and models in the field of TinyML systems :

  1. Memory-Efficient Online Meta Learning: The paper introduces a method for memory-efficient online meta-learning, which enables efficient training on resource-constrained IoT nodes .

  2. Tiny-MLOps Framework: It presents the Tiny-MLOps framework, designed to orchestrate ML applications at the far edge of IoT systems, ensuring efficient management and deployment of ML models .

  3. Error-Driven Input Modulation: The paper discusses error-driven input modulation as a method to solve the credit assignment problem without a backward pass in neural networks .

  4. Incremental On-device Tiny Machine Learning: It explores incremental on-device tiny machine learning as an approach to continuous learning and improvement of ML models on embedded devices .

  5. Semantic Management of On-Device Applications: The paper proposes a semantic management approach for on-device applications in industrial IoT scenarios, enhancing interoperability between IoT devices and ML models .

  6. TinyReptile for Meta-Learning: It introduces TinyReptile, a meta-learning approach for constrained devices, aiming to converge to optimal model weights across different tasks .

  7. TinyMLaaS Ecosystem: The paper discusses the development of a TinyMLaaS ecosystem for machine learning in IoT, addressing research challenges and providing an overview of the ecosystem .

  8. Machine Learning Management: It highlights the importance of ML management in TinyML systems, focusing on transparency in model distribution, scalability, and the relationship between ML models and hardware .

  9. Challenges in Applying TinyML in Production: The paper identifies and addresses challenges in applying TinyML in real-world industrial settings, emphasizing the need for robust strategies beyond developing individual ML models .

  10. Thing Descriptions and Interactions Ontology: It introduces an ontology for the Web of Things, specifically focusing on Thing Descriptions and Interactions, to enhance the description of IoT devices and interactions . The paper "On-device Online Learning and Semantic Management of TinyML Systems" introduces several innovative methods and models with distinct characteristics and advantages compared to previous approaches :

  11. Memory-Efficient Online Meta Learning: The proposed method focuses on memory-efficient online meta-learning, addressing the challenge of limited memory capacity in embedded systems. This approach enables training on resource-constrained IoT nodes, enhancing efficiency in training performance .

  12. Federated Meta-Learning: The paper introduces federated meta-learning, incorporating online learning to enhance model generalization across distributed devices. This approach facilitates rapid learning and optimal performance by leveraging collective knowledge sharing among devices, leading to significant energy, memory, and communication overhead savings .

  13. Semantic Management for TinyML Systems: The study presents semantic management for joint management of models and devices at scale, addressing the challenge of managing diverse characteristics of embedded devices and TinyML models. This semantic approach enhances interoperability, accuracy improvement, resource savings, and reduction in engineering efforts, promoting scalability and shareability of TinyML resources .

  14. Efficiency and Performance: The methods TinyReptile and TinyMetaFed demonstrate rapid local adaptation, outperforming previous approaches by conserving energy, memory resources, and communication overhead. These methods achieve comparable accuracy to existing models while excelling in training speed, resource requirements, and communication efficiency, showcasing significant improvements in training time and energy consumption .

  15. Real-World Applications: The effectiveness of the proposed approaches is validated through real-world TinyML applications such as handwritten character image classification, keyword audio classification, and smart building presence detection. The results confirm the advantages of the methods in terms of accuracy improvement, resource savings, and reduction in engineering effort, highlighting their practical applicability and benefits .

In summary, the paper's methods offer advancements in memory-efficient online meta-learning, federated meta-learning, semantic management, and demonstrate superior efficiency, performance, and applicability in real-world TinyML applications compared to previous approaches.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of TinyML systems and online learning. Noteworthy researchers in this field include Haoyu Ren, Darko Anicic, Xue Li, and Thomas A. Runkler . They have contributed to the study of on-device online learning and semantic management of TinyML systems.

The key solution mentioned in the paper involves addressing the challenges of deploying TinyML models on embedded devices by proposing online learning to enable training on constrained devices, adapting local models to changing field conditions, and introducing federated meta-learning to enhance model generalization across distributed devices . This approach ensures optimal performance among devices by facilitating knowledge sharing and adapting models to evolving input data distributions.


How were the experiments in the paper designed?

The experiments in the paper were designed to benchmark TinyOL with the feature extractor + KNN approach for three applications: Handwritten Character Image Classification, Keyword Spotting Audio Classification, and Smart Building Presence Detection . These experiments were conducted using different devices, such as Raspberry Pi 4 and Arduino Nano BLE 33, to measure various metrics like inference time, training time, energy consumption, and final accuracy . The results of TinyOL were compared with the feature extractor + KNN approach, showcasing the efficiency of online learning on constrained devices with streaming data . The experiments aimed to demonstrate the performance benefits of TinyOL in terms of memory overhead and accuracy improvement across different applications .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is TinyOL. However, it is not explicitly mentioned in the provided context whether the code for TinyOL is open source or not . If you are interested in accessing the code for TinyOL, it would be advisable to refer to the original source of the study or directly inquire with the authors for more information regarding the availability of the code.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper demonstrates the effectiveness of meta-learning, specifically TinyReptile and TinyMetaFed, in rapidly adapting TinyML models to new tasks and conditions across heterogeneous devices . The results show that TinyMetaFed achieves faster and more stable training progress compared to TinyReptile, highlighting the efficacy of the proposed framework . Additionally, the comparison of TinyOL, TinyReptile, and TinyMetaFed assists in selecting the most suitable algorithm for on-device learning based on the application scenarios, emphasizing the practical implications of the research . The experiments not only validate the feasibility of meta-learning in constrained devices but also address computational and communication constraints, enhancing the performance and scalability of TinyML systems .


What are the contributions of this paper?

The paper "On-device Online Learning and Semantic Management of TinyML Systems" makes several key contributions:

  • Online Learning for TinyML: The paper proposes TinyOL, which leverages online learning to enable efficient training on resource-constrained IoT nodes, allowing models to adapt to changing conditions at runtime for robust performance .
  • Federated Meta-Learning: Introduces federated meta-learning that incorporates online learning to enhance model generalization across distributed devices, ensuring optimal performance through knowledge sharing .
  • Semantic Management: Presents semantic management for joint management of models and devices at scale, addressing the challenges of managing diverse characteristics of TinyML systems as they scale up .
  • Real-World Applications: The contributions are validated through real-world TinyML applications such as handwritten character image classification, keyword audio classification, and smart building presence detection, demonstrating effectiveness in accuracy improvement, resource savings, and engineering effort reduction .

What work can be continued in depth?

To delve deeper into the field of TinyML systems, several avenues for further exploration can be pursued based on the existing research:

  • Online Learning Techniques: Further research can focus on enhancing online learning methods like TinyOL to enable efficient training on resource-constrained IoT nodes, allowing models to adapt to changing conditions at runtime for sustained performance .
  • Meta-Learning Integration: Exploring the integration of meta-learning into TinyML systems, such as with frameworks like TinyReptile and TinyMetaFed, can facilitate knowledge aggregation across distributed devices and improve model generalizability while minimizing data requirements .
  • Semantic Management with Semantic Web Technology: Research can be extended to leverage Semantic Web technologies for managing TinyML resources effectively, enabling the joint management of devices and models at scale, enhancing interoperability, and streamlining the management of assets in the TinyML ecosystem .
  • Cross-Domain Expertise and Engineering Effort: Addressing the challenges associated with developing numerous TinyML applications by streamlining processes through integrated solutions like SeLoC-ML, which offers a semantic system for efficient modeling, exploration, and matchmaking of models and devices .
  • Model Reporting and Fairness: Further exploration into areas like model reporting, fairness, and accountability, as highlighted in works such as Model Cards for Model Reporting, can contribute to ensuring transparency and fairness in TinyML applications .

By delving deeper into these areas, researchers can advance the field of TinyML systems, addressing challenges, improving performance, and enhancing the management of resources in industrial settings.

Tables

1

Introduction
Background
Evolution of IoT and the rise of TinyML
Importance of on-device learning in resource-constrained devices
Objective
To explore challenges and advancements in TinyML systems
To present TinyOL, TinyReptile, and TinyMetaFed solutions
To introduce SeLoC-ML for semantic management
Highlight the need for comprehensive strategies in development
Methodology
Data Collection
Real-world case studies and industry trends analysis
Surveys and interviews with developers and practitioners
Data Preprocessing
Gathering TinyML implementation data
Annotating challenges and successes in production environments
TinyOL: On-Device Online Learning
Real-time Adaptation
Dynamic environment adaptation techniques
Performance metrics and evaluation
TinyReptile and TinyMetaFed
Model Generalization
Federated learning approaches for heterogeneous devices
Communication efficiency improvements
Limited labeled data strategies
Case Studies
Success stories and performance enhancements
SeLoC-ML: Semantic Web-based System
Design and Implementation
Semantic management for efficient model-device coordination
Scalability and integration with IoT ecosystems
Evaluation and Results
Accuracy, resource efficiency, and engineering effort improvements
Comparative analysis with existing solutions
Challenges and Future Research
Optimization
Energy consumption, computational complexity, and memory footprint
Model compression and pruning techniques
Robustness
Adversarial attacks and resilience in resource-constrained environments
Data privacy and security considerations
Integration in Industrial Settings
Best practices for deployment and maintenance
Collaboration with industry partners and standardization efforts
Conclusion
Summary of key findings and contributions
Implications for the future of TinyML in IoT
Recommendations for researchers and practitioners
Basic info
papers
databases
distributed, parallel, and cluster computing
machine learning
artificial intelligence
Advanced features
Insights
What does the paper focus on in the context of TinyML systems?
What is SeLoC-ML, and how does it address model-device management in TinyML systems?
What are the two approaches mentioned for enhancing model generalization and communication efficiency in TinyML?
How does TinyOL contribute to real-time adaptation in dynamic environments?

On-device Online Learning and Semantic Management of TinyML Systems

Haoyu Ren, Xue Li, Darko Anicic, Thomas A. Runkler·May 13, 2024

Summary

This paper investigates challenges and solutions for implementing TinyML systems in production, focusing on on-device online learning, federated meta-learning, and semantic management. TinyOL enables real-time adaptation to dynamic environments, while TinyReptile and TinyMetaFed enhance model generalization and communication efficiency across heterogeneous devices with limited labeled data. SeLoC-ML is introduced as a Semantic Web-based system for efficient model-device management at scale. The research showcases improvements in accuracy, resource efficiency, and engineering effort through real-world applications, addressing the need for comprehensive strategies in TinyML development. The work highlights the growing importance of TinyML in IoT devices and the need for further research in optimization, robustness, and integration into industrial settings.
Mind map
Scalability and integration with IoT ecosystems
Semantic management for efficient model-device coordination
Success stories and performance enhancements
Limited labeled data strategies
Communication efficiency improvements
Federated learning approaches for heterogeneous devices
Performance metrics and evaluation
Dynamic environment adaptation techniques
Collaboration with industry partners and standardization efforts
Best practices for deployment and maintenance
Data privacy and security considerations
Adversarial attacks and resilience in resource-constrained environments
Model compression and pruning techniques
Energy consumption, computational complexity, and memory footprint
Comparative analysis with existing solutions
Accuracy, resource efficiency, and engineering effort improvements
Design and Implementation
Case Studies
Model Generalization
Real-time Adaptation
Annotating challenges and successes in production environments
Gathering TinyML implementation data
Surveys and interviews with developers and practitioners
Real-world case studies and industry trends analysis
Highlight the need for comprehensive strategies in development
To introduce SeLoC-ML for semantic management
To present TinyOL, TinyReptile, and TinyMetaFed solutions
To explore challenges and advancements in TinyML systems
Importance of on-device learning in resource-constrained devices
Evolution of IoT and the rise of TinyML
Recommendations for researchers and practitioners
Implications for the future of TinyML in IoT
Summary of key findings and contributions
Integration in Industrial Settings
Robustness
Optimization
Evaluation and Results
SeLoC-ML: Semantic Web-based System
TinyReptile and TinyMetaFed
TinyOL: On-Device Online Learning
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Challenges and Future Research
Methodology
Introduction
Outline
Introduction
Background
Evolution of IoT and the rise of TinyML
Importance of on-device learning in resource-constrained devices
Objective
To explore challenges and advancements in TinyML systems
To present TinyOL, TinyReptile, and TinyMetaFed solutions
To introduce SeLoC-ML for semantic management
Highlight the need for comprehensive strategies in development
Methodology
Data Collection
Real-world case studies and industry trends analysis
Surveys and interviews with developers and practitioners
Data Preprocessing
Gathering TinyML implementation data
Annotating challenges and successes in production environments
TinyOL: On-Device Online Learning
Real-time Adaptation
Dynamic environment adaptation techniques
Performance metrics and evaluation
TinyReptile and TinyMetaFed
Model Generalization
Federated learning approaches for heterogeneous devices
Communication efficiency improvements
Limited labeled data strategies
Case Studies
Success stories and performance enhancements
SeLoC-ML: Semantic Web-based System
Design and Implementation
Semantic management for efficient model-device coordination
Scalability and integration with IoT ecosystems
Evaluation and Results
Accuracy, resource efficiency, and engineering effort improvements
Comparative analysis with existing solutions
Challenges and Future Research
Optimization
Energy consumption, computational complexity, and memory footprint
Model compression and pruning techniques
Robustness
Adversarial attacks and resilience in resource-constrained environments
Data privacy and security considerations
Integration in Industrial Settings
Best practices for deployment and maintenance
Collaboration with industry partners and standardization efforts
Conclusion
Summary of key findings and contributions
Implications for the future of TinyML in IoT
Recommendations for researchers and practitioners
Key findings
12

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address three critical challenges in developing TinyML systems in industries, focusing on efficient adaptation to changing conditions on single devices, handling deployment heterogeneity across multiple devices, and managing TinyML resources effectively over time . While these challenges are not entirely new, the paper proposes innovative approaches based on prior research to tackle these problems in the context of TinyML systems .


What scientific hypothesis does this paper seek to validate?

The scientific hypothesis that this paper aims to validate is related to on-device online learning and semantic management of TinyML systems. The paper focuses on exploring the integration of semantic web technology to handle diverse data sources in TinyML, aiming to enhance interoperability between IoT devices and ML models . The study delves into the development of a semantic ontology tailored for neural network models in IoT scenarios, considering hardware specifications like resource and platform requirements . The research seeks to bridge the gap between IoT devices and ML models by providing a common representation, thereby improving the ecosystem's interoperability .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "On-device Online Learning and Semantic Management of TinyML Systems" proposes several innovative ideas, methods, and models in the field of TinyML systems :

  1. Memory-Efficient Online Meta Learning: The paper introduces a method for memory-efficient online meta-learning, which enables efficient training on resource-constrained IoT nodes .

  2. Tiny-MLOps Framework: It presents the Tiny-MLOps framework, designed to orchestrate ML applications at the far edge of IoT systems, ensuring efficient management and deployment of ML models .

  3. Error-Driven Input Modulation: The paper discusses error-driven input modulation as a method to solve the credit assignment problem without a backward pass in neural networks .

  4. Incremental On-device Tiny Machine Learning: It explores incremental on-device tiny machine learning as an approach to continuous learning and improvement of ML models on embedded devices .

  5. Semantic Management of On-Device Applications: The paper proposes a semantic management approach for on-device applications in industrial IoT scenarios, enhancing interoperability between IoT devices and ML models .

  6. TinyReptile for Meta-Learning: It introduces TinyReptile, a meta-learning approach for constrained devices, aiming to converge to optimal model weights across different tasks .

  7. TinyMLaaS Ecosystem: The paper discusses the development of a TinyMLaaS ecosystem for machine learning in IoT, addressing research challenges and providing an overview of the ecosystem .

  8. Machine Learning Management: It highlights the importance of ML management in TinyML systems, focusing on transparency in model distribution, scalability, and the relationship between ML models and hardware .

  9. Challenges in Applying TinyML in Production: The paper identifies and addresses challenges in applying TinyML in real-world industrial settings, emphasizing the need for robust strategies beyond developing individual ML models .

  10. Thing Descriptions and Interactions Ontology: It introduces an ontology for the Web of Things, specifically focusing on Thing Descriptions and Interactions, to enhance the description of IoT devices and interactions . The paper "On-device Online Learning and Semantic Management of TinyML Systems" introduces several innovative methods and models with distinct characteristics and advantages compared to previous approaches :

  11. Memory-Efficient Online Meta Learning: The proposed method focuses on memory-efficient online meta-learning, addressing the challenge of limited memory capacity in embedded systems. This approach enables training on resource-constrained IoT nodes, enhancing efficiency in training performance .

  12. Federated Meta-Learning: The paper introduces federated meta-learning, incorporating online learning to enhance model generalization across distributed devices. This approach facilitates rapid learning and optimal performance by leveraging collective knowledge sharing among devices, leading to significant energy, memory, and communication overhead savings .

  13. Semantic Management for TinyML Systems: The study presents semantic management for joint management of models and devices at scale, addressing the challenge of managing diverse characteristics of embedded devices and TinyML models. This semantic approach enhances interoperability, accuracy improvement, resource savings, and reduction in engineering efforts, promoting scalability and shareability of TinyML resources .

  14. Efficiency and Performance: The methods TinyReptile and TinyMetaFed demonstrate rapid local adaptation, outperforming previous approaches by conserving energy, memory resources, and communication overhead. These methods achieve comparable accuracy to existing models while excelling in training speed, resource requirements, and communication efficiency, showcasing significant improvements in training time and energy consumption .

  15. Real-World Applications: The effectiveness of the proposed approaches is validated through real-world TinyML applications such as handwritten character image classification, keyword audio classification, and smart building presence detection. The results confirm the advantages of the methods in terms of accuracy improvement, resource savings, and reduction in engineering effort, highlighting their practical applicability and benefits .

In summary, the paper's methods offer advancements in memory-efficient online meta-learning, federated meta-learning, semantic management, and demonstrate superior efficiency, performance, and applicability in real-world TinyML applications compared to previous approaches.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of TinyML systems and online learning. Noteworthy researchers in this field include Haoyu Ren, Darko Anicic, Xue Li, and Thomas A. Runkler . They have contributed to the study of on-device online learning and semantic management of TinyML systems.

The key solution mentioned in the paper involves addressing the challenges of deploying TinyML models on embedded devices by proposing online learning to enable training on constrained devices, adapting local models to changing field conditions, and introducing federated meta-learning to enhance model generalization across distributed devices . This approach ensures optimal performance among devices by facilitating knowledge sharing and adapting models to evolving input data distributions.


How were the experiments in the paper designed?

The experiments in the paper were designed to benchmark TinyOL with the feature extractor + KNN approach for three applications: Handwritten Character Image Classification, Keyword Spotting Audio Classification, and Smart Building Presence Detection . These experiments were conducted using different devices, such as Raspberry Pi 4 and Arduino Nano BLE 33, to measure various metrics like inference time, training time, energy consumption, and final accuracy . The results of TinyOL were compared with the feature extractor + KNN approach, showcasing the efficiency of online learning on constrained devices with streaming data . The experiments aimed to demonstrate the performance benefits of TinyOL in terms of memory overhead and accuracy improvement across different applications .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is TinyOL. However, it is not explicitly mentioned in the provided context whether the code for TinyOL is open source or not . If you are interested in accessing the code for TinyOL, it would be advisable to refer to the original source of the study or directly inquire with the authors for more information regarding the availability of the code.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper demonstrates the effectiveness of meta-learning, specifically TinyReptile and TinyMetaFed, in rapidly adapting TinyML models to new tasks and conditions across heterogeneous devices . The results show that TinyMetaFed achieves faster and more stable training progress compared to TinyReptile, highlighting the efficacy of the proposed framework . Additionally, the comparison of TinyOL, TinyReptile, and TinyMetaFed assists in selecting the most suitable algorithm for on-device learning based on the application scenarios, emphasizing the practical implications of the research . The experiments not only validate the feasibility of meta-learning in constrained devices but also address computational and communication constraints, enhancing the performance and scalability of TinyML systems .


What are the contributions of this paper?

The paper "On-device Online Learning and Semantic Management of TinyML Systems" makes several key contributions:

  • Online Learning for TinyML: The paper proposes TinyOL, which leverages online learning to enable efficient training on resource-constrained IoT nodes, allowing models to adapt to changing conditions at runtime for robust performance .
  • Federated Meta-Learning: Introduces federated meta-learning that incorporates online learning to enhance model generalization across distributed devices, ensuring optimal performance through knowledge sharing .
  • Semantic Management: Presents semantic management for joint management of models and devices at scale, addressing the challenges of managing diverse characteristics of TinyML systems as they scale up .
  • Real-World Applications: The contributions are validated through real-world TinyML applications such as handwritten character image classification, keyword audio classification, and smart building presence detection, demonstrating effectiveness in accuracy improvement, resource savings, and engineering effort reduction .

What work can be continued in depth?

To delve deeper into the field of TinyML systems, several avenues for further exploration can be pursued based on the existing research:

  • Online Learning Techniques: Further research can focus on enhancing online learning methods like TinyOL to enable efficient training on resource-constrained IoT nodes, allowing models to adapt to changing conditions at runtime for sustained performance .
  • Meta-Learning Integration: Exploring the integration of meta-learning into TinyML systems, such as with frameworks like TinyReptile and TinyMetaFed, can facilitate knowledge aggregation across distributed devices and improve model generalizability while minimizing data requirements .
  • Semantic Management with Semantic Web Technology: Research can be extended to leverage Semantic Web technologies for managing TinyML resources effectively, enabling the joint management of devices and models at scale, enhancing interoperability, and streamlining the management of assets in the TinyML ecosystem .
  • Cross-Domain Expertise and Engineering Effort: Addressing the challenges associated with developing numerous TinyML applications by streamlining processes through integrated solutions like SeLoC-ML, which offers a semantic system for efficient modeling, exploration, and matchmaking of models and devices .
  • Model Reporting and Fairness: Further exploration into areas like model reporting, fairness, and accountability, as highlighted in works such as Model Cards for Model Reporting, can contribute to ensuring transparency and fairness in TinyML applications .

By delving deeper into these areas, researchers can advance the field of TinyML systems, addressing challenges, improving performance, and enhancing the management of resources in industrial settings.

Tables
1
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