TinyML: How Singapore Manufacturers are Running AI on Microcontrollers with No Cloud Needed

Machine Learning (ML) has become popular in this era. But have you heard of TinyML? TinyML in Singapore is growing rapidly because it does not require heavy cloud computing. Numerous companies in Singapore are adopting tinyML for support, as it enables real-time data processing. 

In this blog, we will explain the basic functioning of TinyML in Singapore’s industries, the core components required, the benefits, and the challenges. We will discuss various AI/ML courses offered through upGrad that can help you handle these applications for your small business or build a career in AI.

TinyML in Singapore – AI Without the Cloud

Tiny Machine Learning (TinyML) refers to ML models that run on low-power devices called microcontrollers instead of relying heavily on cloud computing. These models analyse data locally and enable real-time decision-making. 

Key Benefits of TinyML in Singapore

TinyML enables local data processing, reducing reliance on cloud computing. A global business hub like Singapore benefits immensely from this technology, which helps process data instantly for manufacturers. It is beneficial in the following ways:

• Real-time data processing helps with efficient decision-making.
• Local data storage reduces operational costs.
• Local data processing reduces cybersecurity risks, thus improving data security.

Challenges to Consider

Like any technology, TinyML also has limitations. Before investing heavily in TinyML, you should consider the challenges involved and make a balanced decision. Some of the challenges posed by this technology are:

• Microcontrollers have limited computing capabilities compared to cloud servers.
• There is a talent gap of professionals with the required expertise.
• There is limited availability of tools and debugging support.

Core Components of TinyML for Manufacturers

TinyML requires some core components to function efficiently. The evolving TinyML Singapore industry requires hardware, software, libraries, deployment models, and maintenance systems to perform effectively.

Hardware – Microcontrollers & Sensors

The main component of TinyML is a microcontroller, a compact battery-powered processor. These act as the device’s brain. Other components are:

• Sensors for measuring temperature, pressure, or vibration
• Accelerators to boost efficiency
• Power management devices to improve battery efficiency

Software – Frameworks & Tools

The software component of TinyML needs to be designed and trained to run efficiently on battery-powered devices. These tools should be lightweight and optimized for microcontrollers.

Some of the components are:

• TensorFlow Lite to deploy ML models
• EdgeImpulse for testing and deploying AI models
• MLOps frameworks for automated model deployment, device monitoring, and lifecycle management on edge nodes.

Deployment – On-Device Inference

TinyML also requires deployment systems that enable AI-trained models to run on microcontrollers. They provide instant alerts about maintenance needs or issues using sensors. They are beneficial as they:

• Reduce dependence on cloud servers
• Enhance data security
• Enable offline operation

Must Read: Machine Learning in Singapore’s Supply Chain Tech Ecosystem: Career Opportunities

Applications of TinyML in Singapore Manufacturing

In this section, we will discuss applications of TinyML in Singapore industries. These low-powered devices don’t require cloud computing. These features have increased the dependence on this new technology. Some of the applications are:

1. Predictive maintenance: Special sensors are attached to devices to enable TinyML-based predictive maintenance in Singapore manufacturing companies. These sensors detect heat, vibration, or acoustic data to send alerts before failure or scheduled downtime. They are beneficial in semiconductor plants.

2. Quality control: These AI-powered sensors can detect defects in products during the production phase. This helps reduce product waste and improve product quality. This is especially beneficial in biomedical device and electronics manufacturing plants that require high precision.

3. Inventory tracking: These AI-powered sensors monitor inventory usage and equipment movement. They improve supply chain operations and are highly beneficial for logistics and warehouse automation.

4. Safety monitoring: These TinyML sensors can be integrated into wearables to monitor worker safety by detecting unsafe conditions or hazardous situations. It helps improve workplace safety and is highly beneficial in industries that use high temperatures or gases.

Also Read: Machine Learning Use Cases for CTOs: Strategic Applications in Singapore Enterprises

Build Your AI and Edge Computing Career with upGrad in Singapore

If you want to build a career in AI/ML, you can rely on courses offered through upGrad, one of the leading educational platforms globally. Some of the best Machine Learning (ML) and Artificial Intelligence (AI) courses offered on our platform are:

1. Master of Science in Machine Learning and AI from Liverpool John Moores University: This Master’s degree has WES recognition and can be completed in 18 months. The benefits of opting for this course are:

• Access to numerous AI tools
• Multiple industry-based projects for hands-on learning
• Complimentary programming course

2. Executive Post Graduate Programme in Machine Learning and AI from IIIT Bangalore: This WES-recognised Diploma course can be completed in 12 months. The benefits of opting for this course are:

• Programming bootcamp option
• Specialisation options: MLOps or Generative AI
• Chance to work on more than 60 case studies

FAQs on TinyML in Singapore