TinyML - The Future of IoT Development

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Quick Navigation

• Why do we need TinyML?
• How TinyML can help improve IoT applications?
• How TinyML works?
• Challenges in integration TinyML with IoT
• TinyML use cases
• Summary

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How TinyML can transform IoT applications across
industries

• Imagine a scenario where you can increase the
  speed of your IoT solutions without burning a
  hole in your customers pocket.
• The one question buzzing through your brain is
  this how.
• Well, by using devices that embed TinyML.

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Why do we need TinyML?

• But before that, we need to ask, why do we need
  machine learning embedded into IoT devices? The
  IoT ecosystem seems to be working fine as of now.
• Well, not exactly.
• It has a few problems of its own, which need to
  be resolved before the world can become a
  connected place in true sense.
• Lets look at what those IoT problems are.

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How TinyML can help improve IoT applications?

• The latest technological advancements in the
  field of Artificial Intelligence present TinyML
  as the bridge between edge computing and smart
  devices, promising to make it even faster. As the
  time taken to do stuff decreases, the energy,
  time and other resources would automatically
  decrease.
• And there is more to it than meets the eye.
  TinyML can help reduce financial, environmental
  and security burdens associated with ML.
• Let us explore in detail at - https//www.techahea
  dcorp.com/blog/tinyml-transform-iot-applications/

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How TinyML works?

• As you already know, training the machine
  learning model is critical to its success. There
  can be no compromise at this stage. After the
  training phase is over, ML is converted to TinyML
  in these four steps

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Challenges in integration Tiny ML with IoT

• A major challenge in integrating TinyML with IoT
  can be attributed to the engineering and
  technological barriers to developing
  microcontrollers the hardware where TinyML
  resides.Challenges in integration Tiny ML with
  IoTA major challenge in integrating TinyML with
  IoT can be attributed to the engineering and
  technological barriers to developing
  microcontrollers the hardware where TinyML
  resides.

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TinyML use cases

• 30 billion microcontroller units were shipped
  in 2019. The boost in microcontroller industry
  has been attributed to the growing demand of
  TinyML for IoT devices. TinyML is revolutionizing
  multiple industries from retail and manufacturing
  to healthcare and fitness.

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Summary

• Tiny machine learning or TinyML is a miniaturized
  version of machine learning algorithms that can
  be embedded in IoT devices. The TinyML algorithms
  are capable of performing the same actions as
  compared to a typical machine learning algorithm
  but at smaller size and complexity. A typical IoT
  ecosystem has three main problems privacy and
  security of data, humongous carbon footprints and
  device latency.

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Visit our website to view full article.

• https//www.techaheadcorp.com/blog/tinyml-transfor
  m-iot-applications/