TinyML Frequently Asked Questions

Q: What is TinyML?

A: TinyML is a branch of machine learning that uses small form factor devices attached with sensors including bare metal or RTOS based microcontrollers to execute ML models and algorithms as software.

Good things, like TinyML, come in small packages.

Another way to look at TinyML technology is as a smaller subset of Machine Learning & Deep Learning technology including software, models, algorithms, applications and devices.

Q: What is a TinyML device?

A: TinyML devices include Microcontrollers and in some cases Programmable logic controllers (PLCs). These controllers are ubiquitous with low-power and compact form factor that allows them to be used as edge devices for low-latency, low-power, and low-bandwidth model inference. Current count of these devices exceed over 300 billion globally.

Future of ML is like a Blackhole - Tiny & Mighty

In contrast to a typical CPU's power consumption of 65 to 85 watts and a typical GPU's power consumption of 200 to 500 watts, a typical microcontroller uses power on the order of milliwatts or microwatts. This is over a thousand time power savings and a thousand times less energy. Due to their low power consumption, tinyML devices can run ML applications on the edge while remaining unplugged from internet or electricity for weeks, months, and in some cases, even years.

Q: What are the popular applications of TinyML?

A: There are over a thousand applications that have gained popularity in over 20 verticals including.

1. Smart Industrial IoT

2. Smart Aviation

3. Smart Farming

4. Smart Transportation and Logistics

5. Smart Security

6. Smart Oil and Gas

7. Smart Environment

8. Smart Space

9. Smart Home

10. Smart City

11. Smart Retail

12. Smart Energy

13. Smart Auto

14. Smart Society

15. Smart Finance

16. Smart Health

A significant number of of verticals are listed in AITS Cainvas.

Q: Is TinyML a open source framework?

A: TinyML is a foundational technology term (similar to embedded) used as an extension to describe other technologies like tinyML devices, tinyML boards, timyML software, tinyML compiler etc. TinyML compilers and interpreters like AITS deepC compiler and Google's TensorFlow Lite Micro are open source.

Q: What is a TinyML device?

A: TinyML devices include Microcontrollers and in some cases Programmable logic controllers (PLCs). These controllers are ubiquitous with low-power and compact form factor that allows them to be used as edge devices for low-latency, low-power, and low-bandwidth model inference. Current count of these devices exceed over 300 billion globally.

Future of ML is like a Blackhole - Tiny & Mighty

In contrast to a typical CPU's power consumption of 65 to 85 watts and a typical GPU's power consumption of 200 to 500 watts, a typical microcontroller uses power on the order of milliwatts or microwatts. This is over a thousand time power savings and a thousand times less energy. Due to their low power consumption, tinyML devices can run ML applications on the edge while remaining unplugged from internet or electricity for weeks, months, and in some cases, even years.

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