When you hear the word machine learning (ML), do you instantly picture a large room of servers, sweating profusely, to crunch huge volumes of data?

It’s fine even if you don’t, as the hardware that makes ML possible is becoming smaller than ever.

Although comparing the sizes of computational devices over the span of decades is quite unfair, the idea here is that the future of ML is more compact and less resource-intensive. This compactness is made possible by a type of ML called TinyML. With tinyML, you can deploy artificial intelligence software directly on edge devices, making it computationally feasible and usable for intelligent apps. 

With TinyML hardware, it is easy to safely keep your machine-learning models with embedded microcontrollers or other small, low-power mechanisms. These devices have less latency, more computational power, and strong data security. As we approach the futuristic era of the quantum revolution, tinyML will be incorporated on a large scale to access data flexibly and directly.

History of TinyML

Initially, researchers and computer scientists used high-end vacuum tubes or electronic numerical integrators and computers (ENIAC) as the US tech revolution came into full force. These bulky devices were also used to store data that was later used for scientific purposes and artificial intelligence.

Here’s the image of a computer used by the first generation of artificial intelligence (AI) researchers in the 1950s.

Source: Wikipedia

Here’s a tiny piece of equipment that’s only a few centimeters long and can run ML today.

Source: QueNube

In other words, TinML can be seen as the marriage between ML and embedded IoT devices. With TinyML, ML models are optimized to run on ultra-low-power and low-memory devices or resource-constrained devices like microcontrollers.

A traditional embedded system is a computing device that consumes minimal energy, making it possible to run it using batteries, such as coin cell batteries, for days, months, or even years.

The easiest way to grasp the idea of TinyML is to think about your smartphone’s voice assistant technology. Whenever you say “OK, Google” or “Hey Siri,” you want your device to respond immediately. For this, the ML model must run locally instead of sending data to a data center for processing.

Examples of TinyML

There are more examples of TinyML being applied to smaller devices around you. For at least a few years now, there have been some sort of artificial neural networks in our smartphones. This makes embedded deep learning viable, making on-device speech recognition and image classification possible. These neural networks are also crucial for capturing high-quality images and improving gesture recognition.

Along with enabling low-power devices to run ML models, TinyML is about running ML inferences in IoT devices by computing data in real-time. This method of processing data closest to the originating source is called edge computing. An ML inference is a process of running (live) data points into an ML model to compute output. It’s also referred to as “putting an ML model into production” or “operationalizing a model.”

TensorFlow Lite, uTensor, AIfES, and Arm’s CMSIS-NN are a few of the most popular frameworks used for deploying ML models in IoT devices. TensorFlow Lite, an open-source deep learning framework created by Google, is the most popular among them. Edge Impulse and OpenMV are some platforms used for TinyML application development.

Albeit, it’s not wise to compare ML and TinyML. Instead, it’s best to see TinyML as a subcategory of ML. That’s because TinyML can’t be used in all instances where ML is currently employed. Think of TinyML as ML in survival mode.

Growth of TinyML

It’s well-known that training ML models and running inferences on them are computationally expensive. Deep learning is hungry for both power and data. The size of state-of-the-art (SOTA) deep learning models witnessed a 10-fold growth year on year. Here’s a graph that shows the increase in computational requirements for SOTA ML models.

Source: OpenAI

Using TinyML to shrink an ML model into smaller embedded devices makes it possible to run low-power model inference. This enables microcontrollers to run for a long time without being charged.

A standard consumer CPU consumes anywhere between 65 watts and 85 watts. A microcontroller consumes a thousand times less power than traditional consumer CPUs, measured in milliwatts or microwatts. As the demand for serverless architecture and edge computing grows, industries will see a spike in TinyML consumption with the recent affirmation of data-driven surveys in the same context. Most publishers contend that the TinyML market is projected to grow at a compound annual growth rate of 13% between 2023-2030.  It will be a spin of future intelligent applications which would require low-bred, computationally inexpensive yet sturdy and secure hardware. 

Benefits of TinyML

Since the data doesn’t have to be sent to a server to run inference, TinyML offers low-latency output. This also means that less internet bandwidth is used. As the data isn’t stored in any servers and the model runs locally, there’s enhanced data privacy as well.

Here are some other benefits of TinyML:

• Lower power consumption: These models are designed to run on microcontrollers that consume the 1000th part of the total processing power of a central processing unit (CPU). This results in significant energy savings as compared to machine learning models that are deployed over a cloud server.
• Extended battery life: Devices that use tinyML have longer battery lives and can operate for longer periods than other traditional hardware devices. 
• Lower latency: By performing inference directly on the device, tinyML reduces the need to send the data to the server. This results in lower latency and faster data retrieval. It also reduces network lags and response times. 
• Reduced delays: Applications such as natural language processing, speech recognition, predictive analytics, and anomaly detection benefit from direct and immediate processing, thus enhancing user experience and system compatibility. 
• Lower bandwidth usage: TinyML stores data locally on your device, that doesn't strain network bandwidth and saves more cost. As you don't store data over internet server, you don't need to pay for data transmission or electronic data exchange and that doesn't exhaust your data packet. 
• Better data privacy: Keeping inference directly on your device reduces the risk of data theft and increases privacy. It also minimizes the risk of unauthorized access and hacking as information stays on the local device. This approach can be followed for industries that host sensitive information like health monitoring, biometric authentication, or security systems.

Tiny specks of a global revolution 

TinyML makes it possible to give intelligence to tiny devices. Since microcontrollers are almost everywhere and collect large amounts of data, TinyML can help better utilize the data collected.

Agriculture, manufacturing, supply chain management, healthcare, transportation, wildlife conservation, and security are some of the fields where TinyML has immense potential. In fact, TinyML has the potential to change the world around us. Imagine every sensor out there with the ability to be smart and reactive to us.

Learn how tinyML can simplify machine learning frameworks to build intelligent applications and scale your AI flywheel for future success.