Embedded AI, often referred to as on-device AI, is revolutionizing the way devices process information and interact with their environment. By integrating artificial intelligence into resource-limited devices—such as wearables, smartphones, smart home gadgets, industrial automation systems, robotics, and autonomous vehicles—embedded AI enables real-time data analysis and decision-making directly on the device.
What Is Embedded AI?
According to the TinyML Foundation:
“Tiny machine learning is broadly defined as a fast-growing field of machine learning technologies and applications—including hardware, algorithms, and software—capable of performing on-device sensor data analytics at extremely low power, typically in the milliwatt range and below, hence enabling a variety of always-on use cases and targeting battery-operated devices.”
Unlike cloud-based AI services that rely on remote servers for computing and data storage, embedded AI processes data locally. This approach reduces latency, conserves bandwidth, and enhances privacy by keeping sensitive data on the device.
Market analysts project significant growth in this sector. Maximize Market Research estimates that the global embedded AI platform market will grow by 5.4% annually, reaching a value of $45.51 billion by 2029, with healthcare and automotive industries being major contributors.
Related Concepts: Edge AI, AIoT, and Embodied AI
While embedded AI focuses on integrating AI into individual devices, several related concepts expand on this idea:
- Edge AI: Incorporates AI into devices at the network’s edge, emphasizing networking capabilities for real-time data processing and decision-making.
- AIoT (Artificial Intelligence of Things): Merges AI with Internet of Things ecosystems, involving multiple interconnected devices and systems.
- Embodied AI: Integrates AI into robotic or physical systems, enabling them to perceive, interact with, and navigate the physical world.
Although these concepts overlap, each has a distinct focus, whether it’s on networking, ecosystems, or physical interaction.
Applications of Embedded AI
Embedded systems are specialized computing setups designed for specific tasks, featuring low-power processors optimized for their intended applications. Here are some domains where embedded AI is making a significant impact:
1. Smartwatches and Fitness Trackers
Real-time activity recognition is a prime example. By analyzing sensor data like accelerometer readings and heart rate, embedded AI algorithms can accurately identify physical activities—such as walking, running, cycling, or swimming—providing users with personalized feedback and insights.
2. Medical Devices
The U.S. Food and Drug Administration (FDA) maintains a list of AI/ML-enabled medical devices. One notable example is an AI-powered whole-breast ultrasound system cleared in 2022. This wearable scanner captures entire breast volumes without needing a trained operator and offers 3D visualizations, aiding physicians in decision-making and patient management.
3. Autonomous Drones
Embedded AI enables drones to perform obstacle avoidance by analyzing real-time sensor data, such as camera feeds, to detect obstacles, people, or other aircraft. This capability allows drones to navigate safely in complex environments without human intervention.
4. Smart Speakers
Keyword spotting involves recognizing specific wake words from audio inputs. Embedded AI allows smart speakers to detect these keywords efficiently and in real-time, enhancing user experience through hands-free interaction.
5. Smartphones
Facial recognition utilizes embedded AI algorithms to analyze and identify unique facial features. This technology is used for unlocking devices and authorizing payments, enhancing security and user convenience.
6. Autonomous Vehicles
In autonomous driving, object detection is critical. Embedded AI systems process data from cameras, lidar, and radar to identify and classify objects like vehicles, pedestrians, and traffic signs. This information is essential for functions such as adaptive cruise control, lane-keeping assistance, and automatic emergency braking.
Advantages of Embedded AI
Embedded AI offers several benefits over cloud-based AI systems:
- Bandwidth Efficiency: Reduces data transmission needs, saving bandwidth and costs, especially in areas with limited connectivity.
- Energy Efficiency: Minimizes energy consumption by performing computations locally, crucial for battery-powered devices.
- Reduced Latency: Processes data in real-time, essential for time-sensitive applications like collision avoidance in vehicles.
- Enhanced Privacy: Keeps sensitive data on the device, reducing the risk of data breaches.
Evaluating Embedded AI Performance
To assess the effectiveness of embedded AI, benchmarks like MLPerf Tiny are used. This suite evaluates performance and efficiency of machine learning inference on resource-constrained devices across tasks like image classification, object detection, and keyword spotting.
Key metrics include:
- Inference Latency: Time taken to process a single input.
- Frames Per Second (FPS): Number of inferences performed per second.
- Accuracy: Correctness of model predictions compared to expected outcomes.
- Power Efficiency: Number of inferences per watt of power consumed.
Technical Enablers of Embedded AI
Three main pillars support embedded AI:
1. Hardware Accelerators
Specialized AI hardware accelerators are designed to enhance the efficiency of AI training and inference processes. Examples include:
- NVIDIA Jetson Series: Offers GPU-accelerated computing for AI applications.
- Google Edge TPU: Provides high-performance ML inferencing on edge devices.
- ARM Cortex-M Series: Optimized for energy-efficient processing in embedded systems.
2. Software Toolchains
These are essential for training and compiling AI models to run efficiently on specific hardware. They handle tasks like optimizing models for memory constraints and converting them into device-compatible formats.
3. Deep Neural Network Optimization Techniques
Optimizing neural networks is crucial for running AI on devices with limited resources. Techniques include:
- Model Compression: Reducing model size through methods like pruning and quantization.
- Network Pruning: Removing unnecessary neurons or connections.
- Parameter Quantization: Lowering the precision of weights and activations.
- Knowledge Distillation: Training a smaller model (student) to replicate the performance of a larger one (teacher).
- Dynamic Computation: Adjusting the model’s complexity based on the computational environment, using methods like dynamic depth and width adjustment.
The Future of Embedded AI
Embedded AI is set to transform industries by bringing intelligence directly to devices and systems. Its ability to offer real-time processing, enhanced privacy, and energy efficiency makes it a pivotal technology for the future. By empowering devices to make intelligent decisions on the spot, embedded AI is paving the way for innovative applications and smarter, more autonomous systems.