Machine Learning in Embedded systems are dedicated computing hardware optimized to carry out specialized functions in bigger systems. With the fast growth of machine learning (ML) and artificial intelligence (AI), embedding ML into embedded systems is creating new ways of smart, efficient, and autonomous devices.
From wearable fitness trackers to self-driving drones and intelligent home appliances, machine learning in embedded systems is transforming industries. In this blog, we will discuss:
- What are Embedded Systems?
- Why Embed Machine Learning in Embedded Systems?
- Challenges of Executing ML on Embedded Devices
- Well-Known Machine Learning Models for Embedded Systems
- Frameworks and Tools for Embedded ML
- Real-World Applications
- Future Trends
What are Embedded Systems?
Embedded systems are small, low-power computing devices designed for specific tasks. Unlike general-purpose computers, they are optimized for real-time performance, reliability, and efficiency. Examples include:
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Microcontrollers (e.g., Arduino, ESP32, Raspberry Pi Pico)
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System-on-Chip (SoC) devices (e.g., NVIDIA Jetson, Qualcomm Snapdragon)
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FPGA-based embedded systems
These systems are used in automotive control units, medical devices, IoT sensors, and industrial automation.
Machine learning on embedded devices
Over the past few years, numerous studies and industrial applications have evolved to increase the efficacy of machine learning algorithms. Previously, massive cloud servers processed all algorithms, but now developers run even complex neural networks and machine learning computations on smaller, power-efficient devices thanks to research breakthroughs. Before we dive into embedded systems, however, let’s first explore the types of platforms that machine-learning models can run on.
Working in the cloud
Machine learning, as they aptly say, is a memory hog and power thirsty, especially when one is dealing with large datasets. For an example, we are designing a machine-learning model for fraud prediction…
The model must process financial data spanning millions of rows and dozens of columns, handling hundreds to thousands of transactions per second during peak periods. That volume-plus-speed combination requires reliable, scalable-up-or-down GPU clusters on demand. Those are where machine learning happens, most times remotely on these computing servers also called “the cloud.”
Over the past few years, numerous studies and industrial applications have evolved to increase the efficacy of machine learning algorithms. Engineers traditionally processed machine learning algorithms on huge servers in the cloud. However, thanks to research breakthroughs now developers can run even the complex computations of neural networks and other machine-learning algorithms on smaller, more power-efficient devices. As a result, we are witnessing a shift toward embedded system in addition before exploring that transition, let us first examine what kinds of machine-learning models can run on these devices.
Shrinking the models
You might’ve heard about various LLMs and see folks mention the number of nodes or connections or something. to refer to how large they are. Large models require large iron to execute, so they are limited to supercomputers and the cloud.
But today what is being done is taking a subset of those models and scaling them down to management size for small devices. You might isolate computer vision and tune it to run on one of the new AI chips that are coming out. After training and optimizing your model, you would then compile it so that it can run exclusively and specifically on that chip.
In 2017, Google released a new version of TensorFlow known as TensorFlow Lite. TensorFlow Lite is designed for embedded and mobile devices. It‘s an open-source, product-ready, cross-platform deep learning framework that transforms a pre-trained TensorFlow model into a specialized format optimized for performance or storage.
Frameworks & Tools
| Framework | Target Hardware | Key Features |
|---|---|---|
| TensorFlow Lite | MCUs, Edge Devices | Quantization, Microcontroller support |
| PyTorch Mobile | Mobile/Embedded | Optimized for ARM CPUs |
| ONNX Runtime | Cross-platform | Supports multiple backends |
| CMSIS-NN (Arm) | Cortex-M MCUs | Highly optimized NN kernels |
| MicroTVM (Apache TVM) | Custom HW | Auto-tuning for efficiency |
The Advantages of Using Machine Learning in Embedded Systems
One of the key advantages of employing machine learning in embedded systems is the capacity for improved efficiency. By processing and analyzing data directly on the device instead of relying solely on cloud computing, these systems can operate faster and more reliably. Moreover, since these systems don’t require constant connectivity, they handle real-world scenarios more effectively, adapting to changing environments and recognizing patterns in sensor data with greater reliability.
Real-Time Processing & Low Latency
Instant Decision-Making – ML models running locally on embedded devices eliminate the need for cloud processing, reducing delays.
Energy Efficiency & Reduced Power Consumption
No Dependency on Cloud Servers – Processing data locally saves energy compared to constant cloud communication.
Enhanced Privacy & Security
Data Stays On-Device – These systems keep sensitive information like biometrics and voice commands on-device, preventing exposure to cloud-based hacking risks.
Cost Savings & Scalability
No Cloud Computing Costs – Reduces expenses on server infrastructure and API calls.
Mass Deployment Feasibility – Thousands of low-cost embedded devices can run ML models independently.
Challenges and Future Possibilities
There are challenges in getting machine-learning applications into embedded systems, despite the many opportunities. We can prevent developement by constraints on power consumption, processing power, and memory resources. However, with consistent improvements in algorithm optimization and hardware design, perhaps embedded systems in the future will be able to deal with these limitations. Many companies are already investing in the development of edge computing solutions aimed toward reducing the machine-learning workload on devices.
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Limited Computational Resources
Microcontrollers often lack floating-point units or GPUs. -
Memory Constraints
Many embedded devices operate with as little as 256 KB of RAM. -
Energy Efficiency
Developers must optimize ML tasks to prevent rapid battery drain. -
Real-Time Requirements
Applications like autonomous drones demand real-time inference with minimal delay.
Embedded machine learning has, in recent years, become popular across different sectors with the advent of ecosystems (for instance, new developments in computer architecture and the advancements in machine learning) that back it with hardware as well as software. This has made it easier to incorporate machine learning models within power–efficient systems like microcontrollers, thus making a wide range of new opportunities.
For app developers who want to develop apps for Internet of Things (IoT) devices, embedded machine learning provides a number of benefits such as reliability, low latency, power savings, data privacy, and no dependency on networks.
FAQ’S
How is machine learning used in embedded systems?
Developers use machine learning in embedded systems to enable devices to make intelligent, real-time decisions locally, without relying on cloud computing. Here’s how they typically apply it:
- Sensor Data Analysis
- Edge Inference
- Implementation Tools
What is an embedding in machine learning?
In machine learning, an embedding is a compact, numerical representation of data (like words, images, or categories) in a lower-dimensional continuous vector space. It captures meaningful patterns, relationships, or semantic similarities in a way that ML models can process efficiently.
Key Properties of Embeddings:
- Dense & Low-Dimensional
- Semantic Meaning
- Learned Automatically
- Transferable
How can AI be used in embedded systems?
AI can be used in embedded systems bring intelligence, autonomy, and adaptability to edge devices, enabling them to operate without constant internet or cloud support. Here’s a breakdown of how and where AI enhances embedded systems:
1. Real-Time Decision Making
2. Voice & Sound Processing
3. Predictive Analytics
4. Autonomous Systems
5. Healthcare Wearables
Will AI replace embedded systems?
AI is unlikely to fully replace embedded systems, but it will increasingly enhance and transform how they operate. Here’s why:
1. Embedded Systems Are Hardware-Centric
2. AI is Becoming a Tool Within Embedded Systems
3. Not All Embedded Tasks Need AI
4. AI Depends on Embedded Systems to Exist
Is there are Embedded Machine Learning jobs ?
Embedded Machine Learning (Embedded ML or TinyML) is a rapidly growing field that blends embedded systems engineering with machine learning. It’s becoming crucial in industries like automotive, IoT, consumer electronics, healthcare, and industrial automation.
| Title | Focus Area |
|---|---|
| Embedded ML Engineer | Deploy ML models on microcontrollers or edge devices |
| Firmware Engineer – ML | Integrate ML inference into firmware running on low-power devices |
| Edge AI Engineer | Optimize and deploy AI models to edge hardware |
| TinyML Developer | Specialize in ultra-low power ML on devices like Arduino, STM32, etc. |
| Computer Vision Engineer (Embedded) | Run CV models on embedded platforms like Jetson Nano, Coral, or mobile SoCs |
| Hardware-Software Co-Design Engineer | Optimize performance across ML model and embedded hardware layers |
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