Date 02/18/24

What is tinyML?

What is tinyML technology?

“We see a new world with trillions of intelligent devices enabled by tinyML technologies that sense, analyze, and autonomously act together to create a healthier and more sustainable environment for all.” 

This is a quote by Evgeni Gousev, senior director at Qualcomm and co-chair of the tinyML Foundation, in his opening remarks at a recent conference.

Machine learning is a subset of artificial intelligence. tinyML aka tiny ml is an abbreviation for tiny machine learning and means that machine learning algorithms are processed locally on embedded devices. 

TinyML is very similar with Edge AI, but tinyML takes Edge AI one step further, making it possible to run machine learning models on the smallest microcontrollers (MCU’s).

Sign up and download our platform for free! 

What is a microcontroller?

Embedded systems normally include a microcontroller. A microcontroller is a compact integrated circuit designed to govern a specific operation in an embedded system. A typical microcontroller includes a processor, memory and input/output (I/O) peripherals on a single chip. 

Microcontrollers are cheap, with average sales prices reaching under $0.50, and they’re everywhere, embedded in consumer and industrial devices. At the same time, they don’t have the resources found in generic computing devices. Most of them don’t have an operating system. 

They have a small CPU, are limited to a few hundred kilobytes of low-power memory (SRAM) and a few megabytes of storage, and don’t have any networking gear. They mostly don’t have a mains electricity source and must run on cell and coin batteries for years. 

What are the advantages with tinyML?

There are a number of advantages with running tinyML machine learning models on embedded devices. 

Low Latency: Since the machine learning model runs on the edge, the data doesn't have to be sent to a server to run inference. This reduces the latency of the output.

Low Power Consumption: Microcontrollers are ultra low power which means that they consume very little power. This enables them to run without being charged for a really long time.

Low Bandwidth: As the data doesn’t have to be sent to the server constantly, less internet bandwidth is used.

Privacy: Since the machine learning model is running on the edge, the data is not stored in the cloud.

Sign up for our free Community Plan and download the Imagimob Studio platform

What are the challenges with tinyML?

Deep learning models require a lot of memory and consumes a lot of power. There have been multiple efforts to shrink deep machine learning models to a size that fits on tiny devices and embedded systems. Most of these efforts are focused on reducing the number of parameters in the deep learning model. For example, “pruning,” a popular class of optimization algorithms, compress neural networks by removing the parameters that are less significant in the model’s output.

The problem with pruning methods is that they don’t address the memory bottleneck of the neural networks. Standard implementations of embedded machine learning require an entire network layer and activation maps to be loaded into memory. Unfortunately, classic optimization methods don’t make any big changes to the early layers of the network, especially in convolutional networks.

What is quantization?

Quantization is a branch of computer science and data science, and has to do with the actual implementation of the neural network on a digital computer, eg tiny devices. Conceptually we can think of it as approximating a continuous function with a discrete one. 

Depending on the bit width of chosen integers (64, 32, 16 or 8 bits) and the implementation this can be done with more or less quantization errors. All modern PCs, smartphones, tablets, and more powerful microcontrollers (MCUs) for embedded systems have a so-called floating-point unit or an FPU. 

This is a piece of hardware next to or is integrated with the main processor, with the purpose to make floating-point operations fast. But for the tiniest MCUs there are no FPUs and instead, one must resort to either of two options: Transform the data to integer numbers and perform all the calculations with integer arithmetic or perform the floating-point calculations in software. 

The latter approach takes considerably more processor time, so go get a good performance on these chips (e.g., ARM’s M0-M3 cores), the former approach is the preferred one. If memory is sparse, which usually is the case on these devices, one can specify the integers to be 16 or 8-bit, thereby reducing the RAM and Flash memory used by the program to half, or a quarter.

What is tinyML software?

tinyML software are typically ultra low power tinyML applications that run on a physical hardware device. There are a large number of machine learning algorithms that are used, but the trend is that deep learning is becoming more and more popular. People that develop tinyML applications are normally data scientists, machine learning engineers or embedded developers. Normally the tinyML models are developed and trained in a cloud system. When the tinyML application runs on the hardware device, the tinyML model can then understand what it was trained for. This is called inference.

What is tinyML hardware?

tinyML can run on a range of different hardware platforms from Arm Cortex M-series processors to advanced neural processing devices. Deep learning normally requires more powerful hardware platforms than other types of machine learning algorithms. IoT devices are an example of tinyML hardware devices. Many people are using an Arduino board to demonstrate machine learning applications.

What is a tinyML framework?

A tinyML framework is a software platform that makes it easier for developers to develop tinyML applications. Tensorflow Lite for microcontrollers is one of the most popular embedded machine learning frameworks.

What is a tinyML development platform?

A tinyML development platform is an end-to-end software platform, where users can develop tinyML applications. They start with collecting data, they can use AutoML to develop the machine learning models and eventually they can deploy the machine learning model on a tiny device. The main benefit with a tinyML platform is that it reduces time-to-market and lower the costs for the developer.

What are some examples of tinyML use cases?

The benefits of tinyML are huge, and the number of use cases for tinyML technology are almost unlimited. Popular use cases include Computer vision, visual wake words, keyword spotters, predictive maintenance, gesture recognition, maintenance of industrial machines and many more.

Learn more about tinyML 

tinyML Foundation is a non-profit organisation that coordinates a lot of activities in the tinyML market. Learn more about tinyML Foundation here. 

Imagimob in tinyML

Imagimob offers Imagimob Studio, a tinyML development platform for machine learning on edge devices and ready-to-go AI models.

Imagimob will continue to support various hardware components from various vendors while now providing an even stronger portfolio based on Infineon’s advanced hardware with its PSoC™ , XMC™ and software (Modus Toolbox) offering. This will provide our existing and new customers the best customer experience by complementing Imagimob's advanced AI platform and AI models with Infineon’s hardware.

Register and download Imagimob Studio via the button below.

Date 07/05/24

Imagimob at tinyML Innovation Forum 2024

Last week Imagimob’s Sales Manager Andreas Nimmerfroh and In...

Date 07/01/24

Imagimob Studio 5.0 has arrived!

With the latest release of Imagimob Studio, we're bringing s...

Date 05/13/24

May release of Imagimob Studio

Date 04/11/24

2024 State of Edge AI Report

Date 03/11/24

What is Edge AI?

Date 03/08/24

March release of Imagimob Studio

Date 02/18/24

What is tinyML?

Date 02/06/24

February release of Imagimob Studio

Date 01/16/24

Introducing Graph UX: A new way to visualize your ...

Date 12/06/23

Imagimob Ready Models are here. Time to accelerate...

Date 01/27/23

Deploying Quality SED models in a week

Date 11/17/22

An introduction to Sound Event Detection (SED)

Date 11/14/22

Imagimob condition monitoring AI-demo on Texas Ins...

Date 11/01/22

Alert Vest – connected tinyML safety vest by Swanh...

Date 10/21/22

Video recording from tinyML AutoML Deep Dive

Date 10/19/22

Edge ML Project time-estimates

Date 10/05/22

An introduction to Fall detection - The art of mea...

Date 04/20/22

Imagimob to exhibit at Embedded World 2022

Date 03/12/22

The past, present and future of Edge AI

Date 03/10/22

Recorded AI Tech Talk by Imagimob and Arm on April...

Date 03/05/22

The Future is Touchless: Radical Gesture Control P...

Date 01/31/22

Quantization of LSTM layers - a Technical White Pa...

Date 01/07/22

How to build an embedded AI application

Date 12/07/21

Don’t build your embedded AI pipeline from scratch...

Date 12/02/21

Imagimob @ CES 2022

Date 11/25/21

Imagimob AI in Agritech

Date 10/19/21

Deploying Edge AI Models - Acconeer example

Date 10/11/21

Imagimob AI used for condition monitoring of elect...

Date 09/21/21

Tips and Tricks for Better Edge AI models

Date 06/18/21

Imagimob AI integration with IAR Embedded Workbenc...

Date 05/10/21

Recorded Webinar - Imagimob at Arm AI Tech Talks o...

Date 04/23/21

Gesture Visualization in Imagimob Studio

Date 04/01/21

New team members

Date 03/15/21

Imagimob featured in Dagens Industri

Date 02/22/21

Customer Case Study: Increasing car safety through...

Date 12/18/20

Veoneer, Imagimob and Pionate in joint research pr...

Date 11/20/20

Edge computing needs Edge AI

Date 11/12/20

Imagimob video from tinyML Talks

Date 10/28/20

Agritech: Monitoring cattle with IoT and Edge AI

Date 10/19/20

Arm Community Blog: Imagimob - The fastest way fro...

Date 09/21/20

Imagimob video from Redeye AI seminar

Date 05/07/20

Webinar - Gesture control using radar and Edge AI

Date 04/08/20

tinyML article with Nordic Semiconductors

Date 12/11/19

Edge AI for techies, updated December 11, 2019

Date 12/05/19

Article in Dagens Industri: This is how Stockholm-...

Date 09/06/19

The New Path to Better Edge AI Applications

Date 07/01/19

Edge Computing in Modern Agriculture

Date 04/07/19

Our Top 3 Highlights from Hannover Messe 2019

Date 03/26/19

The Way You Collect Data Can Make or Break Your Ne...

Date 03/23/18

AI Research and AI Safety

Date 01/30/18

Imagimob and Autoliv demo at CES 2018

Date 05/24/17

Wearing Intelligence On Your Sleeve

LOAD MORE keyboard_arrow_down