abhi Mar 21, 2023 No Comments
A mere century ago, no one could have imagined we would be so reliant on technology. But, here we are, constantly being introduced to some of the smartest, trendiest, and mind-boggling automation procedures. The modern world will come to a screeching halt without the intervention of up-to-the-minute software, framework, and tools. TinyML is the new addition to the category of up-to-date technologies and telecommunications.
There are very few authentic resources available that put light on TinyML. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers, authored by Daniel Situnayake and Pete Warden, is a prestigious and reliable source that answers the question: ‘what is TinyML?’. TinyML is an advancing field that combines Machine Learning and Embedded Systems to carry out quick instructions on limited memory and low-power microcomputers.
Another important feature of TinyML – the only machine learning framework it supports is TensorFlow Lite. Not sure what TensorFlow is? Check the detailed guide on TensorFlow .
Waiting long for machine learning magic is not a pleasant experience in every situation. When regular Machine Learning comes across commands like ‘Okay Google’, ‘Hey Siri’, or ‘Alexa’, the response can be time-intensive. But, the goal is quick responses from small directions like these. The desired fast reaction is only possible when the TinyML application is in effect.
It’s time to dive deep into the discussion of TinyML:
What is TinyML?
TinyML is a specialized study of Machine Learning that sits in the middle of embedded systems and machine learning (ML). It enables expansion and disposition of complex ML models on low-power processors that have limited computational abilities and memory.
TinyML allows electronic accessories to overcome their shortcomings by gathering information about the surroundings and functioning as per the data collected by ML algorithms. It also enables users to enjoy the benefits of AI in embedded tools. The simplest answer to ‘what is TinyML?’: TinyML is a framework to safely transfer knowledge and intelligence in electronic devices using minimal power.
The rapid growth in the software and hardware ecosystems enables TinyML application in low-powered systems (sensors). It warrants a real-time response, which is highly in demand in recent times.
The reason behind the growing popularity of TinyML in the real world is its ability to function perfectly fine without necessitating a strong internet connection, and massive monetary and time investment. It is rightly labeled as a breakthrough in the ML and AI industry.
TinyML has successfully addressed the shortcomings of standard Machine Learning (ML) models. The usual ML system cannot perform its best without entailing massive processing power. The newest version of ML is ready with its superpower to take over the industry of edge devices. It does not disappoint by demanding manual intervention such as connecting the tool to a charging point just to process simple commands or perform small tasks.
The application enables the prompt performance of minute but integral functions while eliminating massive power usage. A father figure in the TinyML industry, Pete Warden says, TinyML applications should not necessitate more than 1 mW to function.
If you are not well-versed in the basic concept of machine learning, our blog might help you understand it better.
New-age data processing tools and practices (Data Analytics, Data Engineering, Data Visualization, Data Modeling) have become mainstream due to their ability to offer instant solutions and feedback.
TinyML is solely based on data computing ability; it’s just faster than others. Here are a few uses of TinyML that we all are familiar with but probably, were not aware of the technology behind these:
Usually, a user anticipates an instant answer or reaction from a system/device when a command is stated. But a thorough process involving the transmission of instructions to the server and device capacitates the outcome. As one can easily fathom that this long process is time-consuming and thus the response gets delayed sometimes.
TinyML application makes the entire function simple and fast. Users are only concerned with the response; what goes inside does not pique the interest of many. Modern electronic gadgets that come with an integrated data processor are a boon of TinyML. It encourages the fast reaction that customers are fond of.
The exhaustive system of data management, transmission, and concocting can be intense. It also accelerates the risk of data theft or leak. TinyML safeguards user information to a great extent. How? The framework allows data processing in the device. The growing popularity of Data Engineering has also skyrocketed the need for safe data processing. From an entirely cloud-based data processing system to localized data processing, data leak is not a common problem for users anymore. TinyML erases the need to secure the complete network. You can now get away with just a secured IoT device.
A comprehensive server infrastructure is an ultimate foundation to ensure safe data transfer. As TinyML reduces the need for data transmission, the tools also consume less energy compared to the models manufactured before the popularity of the field. The common instances where TinyML is in use are microcontrollers. The low-power hardware uses minimal electricity to perform its duties. Users can go away for hours or days without changing batteries, even when they are in use for an extended period.
Regular operations using ML demand a strong internet connection. But, not anymore when TinyML is in action. The sensitive sensors seize information even without an internet connection. Thus, no need to worry about data delivery to the server without your knowledge.
Though it’s almost perfect, but not free from flaws. When the world is fascinated by the potential of TinyML and constantly seeking answers to ‘what is TinyML?’; it’s important to keep everyone informed of the challenges the framework throws at users. Combing through the internet and expert views, a few limitations of TinyML have been listed here:
Regular ML models use a certain amount of power that industry experts can predict. But TinyML does not leverage this advantage as each model/device uses different amounts of electricity. Thus, forecasting an accurate number is not possible. Another challenge users often face is an inability to determine how fast they can expect the outcome of commands on their device.
The small size of the framework also limits the memory storage space. Standard ML models weed out such complications.
The current retail chains manually monitor the stocks. The precision and accuracy of state-of-the-art technologies (such as TinyML) deliver better results compared to human expertise. Tracking inventories becomes straightforward when tinyML is in action. The introduction of footfall analytics and TinyML has transformed the retail business.
TinyML can be a game-changer for the farming industry. Whether it’s a survey of the health of farm animals or sustainable crop production, the possibilities are endless when the latest technologies are combined and adopted. 
The smart framework expedites factory production by notifying workers about necessary preventative maintenance. It streamlines manufacturing projects by implementing real-time decisions. It makes this possible by thoroughly studying the condition of the equipment. Quick and effective business decisions become effortless for this sector.
TinyML application simplifies real-time information collection, routing, and rerouting of traffic. It also enables fast movement of emergency vehicles. Ensure pedestrian safety and reduce vehicular emissions by combining TinyML with standard traffic control systems.
Experts believe we have a long way to go before we can claim TinyML as a revolutionary innovation. However, the application has already proved its ability and efficiency in the machine learning and data science industry. With an answer to the question ‘what is TinyML?’, we can expect the field to advance and the community to grow. The day is not far away when we will witness the application’s diverse implementation that none has envisaged. TinyML is ready to go mainstream with the expansion of supportive programming tools.
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