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Tiny Machine Learning Market Size, Share, and COVID-19 Impact Analysis, By Component (Solution and Services); By Application (Retail, Healthcare, Agriculture, Manufacturing, and Others); and Regional Forecast, 2024-2032

Region :Global | Report ID: FBI107067 | Status : Ongoing



Tiny Machine Learning (TinyML) is a subset of machine learning which pursues running machine learning applications on low-powered devices like microcontrollers. It is a fast-growing research area that has recently gained popularity. TinyML provides various advantages, such as low latency, low power consumption, privacy, and low bandwidth.

Even though TinyML is an emerging field, it has been in production for years. It is used in gesture recognition, audio detection, keyword spotting, machine monitoring, object recognition, and classification. Some examples of TinyML are OK Google, Hey Siri, Alexa, and other wake words.

The market growth is driven by the rising number of IoT devices and advancements in machine learning technologies. Moreover, less power consumption aids the TinyML devices to run unplugged on batteries for a long time while running ML applications on edge. It improves the productivity of deep learning artificial intelligence (AI) systems by needing less computation, less data, and fewer engineers to facilitate the big market of edge AI and IoT.

For instance, McKinsey states that 40% of the annual value created by analytics is made up of deep machine learning techniques.

Impact of COVID-19 on the Tiny Machine Learning Market

COVID-19 has positively impacted the market, with the number of machine learning applications widely increasing in the healthcare sector. Machine Learning is used in the healthcare sector to predict diseases and care for covid patients accurately.

TinyML was used as a platform to tackle and combat the pandemic. Several countries use artificial intelligence and machine learning to track, trace and understand covid cases. The pandemic has witnessed a significant acceleration in the adoption of TinyML applications.

For instance,

  • According to The Brookings Institution report, researchers used CCTV camera footage and geolocation data in South Korea to track covid patients.

  • French start-up, Clevy.io, launched a chatbot for people to find out about communications given by the government on COVID-19.

However, the hardware AI market has disrupted the supply chain, with trade restrictions imposed on several countries. Data Forecast's 2020 AI hardware chip global revenue fell by 12% due to COVID-19.

Key Insights

The report will cover the following key insights:

  • Micro Macro Economic Indicators.

  • Drivers, Restraints, Trends, and Opportunities.

  • Business Strategies Adopted by the Players.

  • Impact of COVID-19 on Tiny Machine Learning Market.

  • Consolidated SWOT Analysis of Key Players.

Analysis by Application

Based on application, the market is segmented into retail, healthcare, agriculture, manufacturing, and others. The healthcare sector holds the maximum share in the market and is expected to grow at the highest rate in the forecasted period. The segmental growth is due to the rising applications of TinyMl in the healthcare sector. It can be used in diagnosing and detecting diseases. For instance,

  • Closedloop, an AI start-up, used TinyML and artificial intelligence to identify those at the highest risk of severe complications from COVID-19.

  • BlueDot, a Canadian start-up, used AI and machine learning algorithms to detect disease outbreaks.

Regional Analysis

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The global TinyML market is divided into five regions: North America, Europe, Asia Pacific, Middle East & Africa, and South America. North America holds the most significant market share due to the presence of leading technology providers in the U.S. and Canadian countries. Furthermore, the region leads in the early adoption of advanced technologies, accelerating market growth. There is an increasing demand for these solutions in the automotive sector. It aids in predictive maintenance, supply chain management, and quality control. For instance,

  • Escorts Group implemented machine learning and artificial intelligence solutions to enable working capital from inventories in its agricultural machinery business.

  • Mahindra & Mahindra uses TinyML and AI-enabled dynamic segmentation capability to optimize its spare parts inventories.

The distribution of the Tiny Machine Learning market by region of origin is as follows:

  • North America – 49%

  • Europe – 26%

  • Asia Pacific – 16%

  • The Middle East and Africa – 6%

  • South America – 3%

Key Players Covered

The major global TinyML market companies include Google LLC, Microsoft Corporation, ARM, STMicroelectronics, Cartesian, Meta Platforms, EdgeImpulse Inc., InData Labs, Amazon Web Services, Databricks, ScienceSoft, MobiDev, and others.


By Component

By Application

By Geography

  • Solution

  • Services

  • Retail

  • Healthcare

  • Agriculture

  • Manufacturing

  • Others (BFSI, Automotive)

  • North America (U.S., Canada, and Mexico)

  • Europe (U.K., Germany, France, Italy, Spain, Russia, Benelux, Nordics, and Rest of Europe)

  • Asia Pacific (China, India, Japan, South Korea, ASEAN, Oceania, and Rest of Asia Pacific)

  • Middle East & Africa (Turkey, Israel, GCC, North Africa, South Africa, and Rest of MEA)

  • South America (Brazil, Argentina, and Rest of South America)

Key Industry Developments

  • In June 2022: Renesas Electronics Corporation, a semiconductor solutions supplier, acquired Reality Analytics, Inc., an AI solutions provider. With this acquisition, Renesas Corporation can access and utilize Reality AI's artificial intelligence and Tiny Machine Learning (TinyML) solutions.

  • In January 2022: Klika Tech, an IoT and cloud-based product and solutions development company, joined the TinyML foundation to increase the development of edge computing with machine learning. Klika Tech can access on-device data analytics through machine learning through this partnership.

  • Ongoing
  • 2023
  • 2019-2022

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