Boost your AI-powered TinyML edge devices and applications prototyping

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Demo request What is TinyML? How we can help?
Picture by Deepak Pal

What is TinyML?

TinyML is a technique or field of study in machine learning and embedded systems that explores which machine-learning applications (once reduced, optimized and integrated) can be run on devices as small as microcontrollers.

What are the upsides of TinyML over cloud-computing ML?

  • Quick Data Processing: Because the reduced, optimized machine-learning applications in TinyML run on edge computing, data processing is efficient. Only necessary or desired data, if any at all, is sent to long-distance cloud servers for storage.
  • Low Internet Bandwidth: TinyML requires low bandwidth since data is less frequently sent to long-distance cloud servers.
  • Low Power Requirements: Microcontrollers are the vehicles for TinyML models and have low energy requirements, which reduce charging length and frequency.
  • Data Privacy: TinyML applications run on the edge and either do not store data on long-distance cloud servers or only send select data back to the servers.

What do you need to get started?

  • Microcontroller: on which to run TinyML models. For instances: Arduino Nano 33 BLE Sense, Portenta H7, STM32L476RG or equivalent STM32 mbed-based boards, ESP32, ...
  • ML Model: any model that can be converted into a TinyML version.
  • Modeling framework: to run models on microcontrollers: Tensorflow Lite Micro, CoreML, PyTorch Mobile, ...

What are the Market perspectives?*

In 2030, ABI Research predicts the shipment of approximately 2.5 billion devices that feature TinyML. And, in just the next five years, Silent Intelligence forecasts that TinyML could "reach more than $70 billion in economic value."

Uses for TinyML are far-ranging and address convenience, communication, knowledge-sharing, matchmaking, entertainment, agriculture, predictive maintenance, and healthcare, to name a few. Today, the most common fields for TinyML application include audio analytics, pattern recognition, and voice human-machine interfaces. Here are just a few of the many compelling ways TinyML can improve processes, reduce costs, and increase the quality of life:

  • Predictive maintenance in manufacturing and other industries through the use of low-power sensors on equipment and machinery
  • Building automation in lighting, HVAC, and other applications
  • Vision, motion and gesture recognition in toys and entertainment
  • Pharmaceutical development and testing
  • Audio analytics in child and elderly care
  • Identifying and preventing the spread of illness in healthcare
  • Assessing the status of agricultural crops in farming healthcare

How IoTec-AI can help?

Building an AI-powered device from scratch is a complex and time-consuming task. You need to build a full-featured dataset, create and train the machine learning model, optimize it for the smart device, develop an appropriate application, flash this application to the mobile or edge device and test everything. Here below a typical workflow.

Workflow

Furthermore, this is a long-running process involving multiple profiles: data scientists, embedded device software engineers but also product designers, testers, project managers, ...

IoTec-AI will help you in providing a collaborative online workbench for prototyping your smart edge device applications rapidly, supporting different dataset types (e.g. motion or environment data, audio, images, video), machine or deep learning algorithms , hardware development platforms, boards or NN accelerators (e.g. STMicroelectronics, Nvidia Jetson, Intel Movidius, Google Coral, Android/iOS, Raspberry pi or Arduino) in various business domains (e.g. Security, Health, Smart cities, Aerospace, IIoT, Automotive)

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