Summary: TinyML is the latest technology that helps to transform IoT devices by making them perform faster. But, what TinyML exactly is. Let’s know this latest technological terminology and how it is helping businesses in the digital world.
Do you know that you can increase the accuracy and speed of your client’s IoT solutions without spending much? You must be thinking – HOW?
Well, the answer is you can use devices that integrate TinyML. Now, the question that’s buzzing again in your mind is – what is TinyML? Right?
If you are not familiar with TinyML and don’t know how it transforms your IoT apps, here is the complete information.
What is TinyML?
TinyML is an abbreviation used for Tiny Machine Learning algorithms. It’s an emerging discipline that focuses on reducing machine learning algorithms to a volume where it can embed in IoT solutions. It is also referred to as embedded AI (Artificial Intelligence) or edge AI because they provide AI capabilities to the device where it is embedded.
Don’t you feel that it’s an oxymoron concept?
The idea behind IoT is to keep things small yet energy-efficient. Machine learning algorithms are growing rapidly in size, volume, and complexities. This is certainly due to the arrival of big data, the rising volume of data across various channels, and the exponential increase in the compute power of graphical processing units.
Read: Deep Learning and Training Bots: A New Look
Edge computing is constantly contributing towards making IoT faster; so, will TinyML really make a difference?
But first, we need to know what leads to embed machine learning in IoT devices. Isn’t the IoT system working excellently as of now?
Not exactly!
Before the entire world becomes a connected place with the invention of robust IoT devices and the evolving ecosystem, it’s critical to resolve a few issues that these devices encounter.
Let’s discuss these key problems of IoT solutions.
Problems of IoT Solutions
Carbon Footprints
Or training purposes, the enormous size of dynamic machine learning algorithms require a massive amount of energy. The GPT-3 algorithm, released in May 2020, comprises a network architecture that contains 175 billion neurons. That’s more than twice the number of neurons in the human brain. GPT-3 algorithm costs around 10 million dollars and consumes about 3 GWh (Gigawatt hours) of electricity.
According to research at the University of Massachusetts in 2019, training one deep learning model generates huge amounts of CO2 emissions that amount to 626,155 pounds (approximately). This equals the carbon emissions from more than five cars over their lifetime.
It’s important to have a scientifically proven, sustainable system in place to ensure stable technological growth for a better world.
Data Privacy & Security
The primary function of IoT solutions is to collect data across different channels. The data generated and gathered by IoT endpoints is shared with the Cloud Servers for further processing. When data is transmitted to the Cloud, a lot of security vulnerabilities enters the environment.
Security is at stake when the data travels or at-rest. Hackings attempts can result in compromising business-critical data. IBM Cost of Data Breach Report 2020 states that the average data breach cost amounts to 3.86 million dollars.
Latency
When you say “Hey Alexa”, the first thing that the device does is checking the Internet connectivity. When you instruct the IoT device to do something, the instruction is sent to the Server, which then executes the instruction and returns the relevant information to the device.
This specific time that lags between sending an instruction to the Server and getting a response is called Latency. If the network is fast, the device will have low latency, whereas, if the network connectivity is slow, latency will be high.
Since IoT devices are programmed to perform faster with a focus on speedy user-engagement, high latency is undesirable.
How TinyML Helps Improve IoT Solutions?
TinyML, being the latest technological innovation in the area of Artificial Intelligence, bridges the gap between edge computing and smart devices to make them perform faster. As the time that IoT device takes to execute an instruction decrease, the energy and compute resources decrease automatically.
In addition to this, TinyML reduces the operational, environmental, security, and financial burdens in comparison to ML.
TinyML is used extensively in IoT devices such as health gadgets, CCTV cameras, etc., that run over interactive mobile apps. However, an expert .Net development company from India can help integrate the latest machine learning algorithms and build a multi-spectral mobile application that boosts revenue.
Energy Efficient
Modifying the entire stack of ML algorithms can reduce the carbon footprints of machine learning without compromising reliability and accuracy. It includes complete transformation from hardware components to software applications.
Isn’t it time-consuming and involves a lot of investment? This is where TinyML enters in with the intent to shrink machine learning algorithms to minimize the carbon footprints.
Reduced Latency
As we have discussed earlier, latency is the time lag between the IoT devices sending and receiving data. With its own processing power, the device doesn’t require sending every single instruction to the Cloud Servers for further processing and execution.
As the dependency of the device on network connectivity for execution reduces, the latency also decreases.
Better Data Privacy & Security
During transit, data is most vulnerable and involves multiple security threats. TinyML minimizes the hacking attempts, making data as well as the network more secure by safeguarding IoT devices and Cloud infrastructure.
Collects Essential Data Only
Data Analytics reports include a huge amount of data. A lot of data in these reports is redundant. With TinyML, intelligently programmed IoT devices are designed to collect useful data from the most triggering events.
Also, Read: Software Testing and Healthcare: What is there in the Future?
Applications of TinyML
TinyML is a revolutionary concept that is transforming multiple industries from manufacturing and finance to fitness and healthcare.
- Manufacturing: In the manufacturing industry, TinyML is used or monitoring equipment in real-time and reduces downtime.
- Retail: TinyML can be used to monitor shelves in retail stores and managing inventory automatically.
- Healthcare: In the healthcare sector, TinyML can monitor patients’ health in real-time and send out alerts if an emergency arises.
- Transport: TinyML sensors installed at traffic signals can help route traffic, improve emergency response, reduce congestion, and reduce pollution.
Wrapping Up
Though TinyML is a miniaturized version of the Machine Learning (ML) algorithms, it is capable of performing the same powerful functions as performed by ML- at a smaller size and reduced complexity. TinyML can strengthen IoT solutions by improving data security and safety, collecting important data, making them more energy-efficient and reducing latency.
