A review of TinyML

• URL: http://arxiv.org/abs/2211.04448v1
• Date: Sat, 5 Nov 2022 06:02:08 GMT
• Title: A review of TinyML
• Authors: Harsha Yelchuri, Rashmi R
• Abstract summary: The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware. This paper explores how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this current technological world, the application of machine learning is becoming ubiquitous. Incorporating machine learning algorithms on extremely low-power and inexpensive embedded devices at the edge level is now possible due to the combination of the Internet of Things (IoT) and edge computing. To estimate an outcome, traditional machine learning demands vast amounts of resources. The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware centered on deploying deep neural network models on embedded (micro-controller-driven) systems. TinyML will pave the way for novel edge-level services and applications that survive on distributed edge inferring and independent decision-making rather than server computation. In this paper, we explore TinyML's methodology, how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope.

Related papers

• On-device Online Learning and Semantic Management of TinyML Systems [8.183732025472766]
  This study aims to bridge the gap between prototyping single TinyML models and developing reliable TinyML systems in production. We propose online learning to enable training on constrained devices, adapting local models towards the latest field conditions. We present semantic management for the joint management of models and devices at scale.
  arXiv  Detail & Related papers  (2024-05-13T10:03:34Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• TinyML: Tools, Applications, Challenges, and Future Research Directions [2.9398911304923456]
  TinyML is an embedded machine learning technique that enables ML applications on cheap, resource- and power-constrained devices. This article reviews various avenues available for TinyML implementation.
  arXiv  Detail & Related papers  (2023-03-23T15:29:48Z)
• OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System [85.8338446357469]
  We introduce OmniForce, a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques. We show how OmniForce can put an AutoML system into practice and build adaptive AI in open-environment scenarios.
  arXiv  Detail & Related papers  (2023-03-01T13:35:22Z)
• Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots [57.27442333662654]
  Machine learning (ML) has become a pervasive tool across computing systems. Tiny robot learning is the deployment of ML on resource-constrained low-cost autonomous robots. Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints. This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.
  arXiv  Detail & Related papers  (2022-05-11T19:36:15Z)
• How to Manage Tiny Machine Learning at Scale: An Industrial Perspective [5.384059021764428]
  Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers. TinyML models have been developed with different structures and are often distributed without a clear understanding of their working principles. We propose a framework using Semantic Web technologies to enable the joint management of TinyML models and IoT devices at scale.
  arXiv  Detail & Related papers  (2022-02-18T10:36:11Z)
• Widening Access to Applied Machine Learning with TinyML [1.1678513163359947]
  We describe our pedagogical approach to increasing access to applied machine-learning (ML) through a massive open online course (MOOC) on Tiny Machine Learning (TinyML) To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds.
  arXiv  Detail & Related papers  (2021-06-07T23:31:47Z)
• TinyML for Ubiquitous Edge AI [0.0]
  TinyML focuses on enabling deep learning algorithms on embedded (microcontroller powered) devices operating at extremely low power range (mW range and below) TinyML addresses the challenges in designing power-efficient, compact deep neural network models, supporting software framework, and embedded hardware. In this report, we discuss the major challenges and technological enablers that direct this field's expansion.
  arXiv  Detail & Related papers  (2021-02-02T02:04:54Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)
• From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks [71.23327876898816]
  Federated learning has emerged as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data. We advocate a new learning paradigm called fog learning which will intelligently distribute ML model training across the continuum of nodes from edge devices to cloud servers.
  arXiv  Detail & Related papers  (2020-06-07T05:11:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.