Edge AI Explained: A Complete Introduction

It’s not an exaggeration to say things happen fast in tech nowadays. Edge AI is one of the driving forces behind some of the great innovations. Estimates say that 41.6 billion IoT devices will produce an unbelievable 200 million terabytes of data every day.

Imagine the pressure on networks if everything relied on cloud computing. That’s where Edge AI comes in to handle data processing directly on devices. In fact, while only 10% of data was processed on the edge in 2021, that number is expected to jump to 75% by 2025.

What is Edge AI?

Edge AI is an extension of the concept of edge computing. Edge computing is processing data at the edge of a network. The edge of a network means either by the device itself or a local server, not the cloud.

AI processing is commonly done in the cloud as it requires significant computational resources. However, in Edge AI, AI processing is done on the device itself by moving the resources directly to the device.

Edge AI is used in devices like smartphones, cameras, and industrial sensors. Edge AI helps to:

• Reduce delays
• Minimize internet usage
• Increase privacy

How Edge AI differs from cloud-based AI

The main difference is how machine learning models are deployed and processed.

Traditional AI heavily relies on cloud-based infrastructure. Data is sent to remote servers that require powerful resources like GPUs. This approach often struggles with latency, security concerns, and constant internet dependence.

Edge AI processes data locally on devices like smartphones or IoT sensors. This significantly reduces latency and provides faster real-time responses. It improves data privacy by keeping information on the device. Moreover, Edge AI minimizes bandwidth usage which makes it ideal for scenarios with limited or unreliable internet access.

Real-world use cases of Edge AI

Smart speakers and virtual assistants - Edge AI is used to implement the always-listening feature in devices like Amazon Alexa. These devices run lightweight models locally to detect wake words like "Alexa." The local processing provides quick, real-time responsiveness, which is required for these functionalities to work successfully.

Wearables for health and fitness tracking - Smartwatches and fitness trackers use Edge AI to monitor sleep patterns and physical activities. Many of these devices run simple models directly on the device. For example, with Edge AI, step counting or basic sleep tracking can be performed locally with fast and efficient feedback to the user.

Automated optical inspection in manufacturing - Edge AI supports defect detection in manufacturing processes. Smart cameras with Edge AI identify issues like packaging errors or misaligned pallets in real-time. 

Predictive maintenance for machinery - Edge AI analyzes sensor data like electrical current, vibrations, and sound, to monitor machinery for potential issues. This data is processed locally to detect anomalies. 

Autonomous vehicles and robotics - Autonomous vehicles and robots rely heavily on Edge AI for decision-making in environments where stable internet connections cannot be guaranteed. Most of the processing such as navigation, obstacle detection, and object recognition, occurs directly on the vehicle or robot. 

Key steps in developing edge AI applications

1. Identify the specific problem the application will address. Eg;- Detect manufacturing defects in real time using camera-equipped IoT devices. 
2. Next, collect high-quality datasets for training AI models. This data can be taken directly from target edge devices or simulated environments. For example, sensor data from machines to predict faults or monitor performance.
3. Then use frameworks like TensorFlow or PyTorch to train machine learning or deep learning models.
4. Apply optimization techniques like quantization, pruning, and model compression to reduce the size and computational requirements of the model. These techniques help the model to run smoothly on edge devices with limited resources.
5. Choosing the appropriate hardware for your Edge AI application is important. Depending on the AI workload, this could include microcontrollers, single-board computers, or specialized AI accelerators. 
6. Next, we need to deploy the trained and optimized AI model to the edge devices. Once deployed, test for latency, accuracy, and reliability in real-world scenarios. 
7. To enhance the application further connect it to cloud IoT platforms such as AWS IoT Greengrass, Azure IoT Edge, or Google IoT Core. These platforms support remote model updates, data storage, and scalability.

Benefits of edge AI for end users

Edge AI brings many benefits that enhance both the user experience and overall efficiency, which can be summarized as speed, privacy, cost savings, and reliability.

• Function autonomously on local devices - Edge AI operates directly on local devices without needing constant cloud connectivity. For continued learning and development, the device can still send data back to the cloud by establishing a feedback loop that will eventually improve the system's intelligence and accuracy.
• Faster responses - Processing data locally reduces latency, which results in faster responses. For example, Edge AI in autonomous vehicles can quickly react to road conditions, hazards, or blockages by processing sensor data locally.
• Better privacy - Again, as the data is processed locally, sensitive data is safer with Edge AI. Using cloud-based systems in traditional AI always carries a higher risk of exposure and security breaches.
• Cost savings - Local data processing decreases the need for large storage and higher bandwidth which is the case when working with the cloud.
• Reliable performance - Edge AI offers consistent and dependable performance, even in an unstable or no-internet environment. It is ideal for remote locations like rural areas or industrial sites. For example, Edge AI can be used in agriculture to track livestock health or soil conditions in places without relying on external networks.

(Related reading: reliability metrics.)

How does Edge AI lead the way in AI performance?

Edge AI is an emerging AI adaptation not only for its key advancements but also to accelerate AI performance.

For example,  as per this article, applying 8-bit quantization to AI models has shown up to a 50% reduction in power consumption while maintaining acceptable prediction performance. Moreover, a study on Jetson edge devices demonstrated that an end-to-end video-based anomaly detection system achieved an inference speed of 47.56 frames per second (FPS) with only 3.11 GB RAM usage. This resulted in a 15% performance improvement and 50% lower energy consumption compared to the previous version.

Integrating Edge AI with IoT, 5G, and federated learning

With the advancement of technology, the integration of IoT and 5G with Edge AI has become unavoidable. IoT devices provide real-time monitoring and continuous data collection from various systems.

Meanwhile, 5G connectivity provides:

• High bandwidth
• Low latency
• Support for massive device networks

Together, these technologies improve data transfer capabilities and enable edge devices to process multiple streams of data simultaneously.

(Learn more about IoT monitoring.)

Federated learning is a machine learning method that trains models on multiple devices without sharing raw data. It is often sought for improving data security and privacy. Edge AI can benefit greatly from federated learning, especially when used in sensitive fields like healthcare and finance.

(Related reading: federated AI, federated data, & federated search.)

Energy consumption in Edge AI

Efficient use of energy is a big challenge in Edge AI, especially when dealing with battery-powered devices like drones, wearables, and IoT sensors. Here are some strategies that can be used to optimize energy consumption.

Model optimization

With Edge AI and all other AI-based applications, it is a standard procedure to address computational requirements without compromising accuracy. Quantization, pruning, and model compression are some examples of model optimization. Maintaining the model as simple as possible with fewer layers is also important to reduce energy consumption.

Hardware efficiency

Choose low-power components such as GPUs, TPUs, or ASICs designed for Edge AI to match performance needs while conserving energy.

Dynamic workloads

Don’t let the system run continuously. Activate systems only when needed. For example, activate intelligent surveillance systems only when motion is detected.

Edge-cloud collaboration

Balance processing between the edge and the cloud according to needs. For example, fitness bands should use edge devices to provide real-time predictions but perform detailed analysis in the cloud.

Drawbacks of Edge AI

Edge AI, despite its many advantages, is not without its drawbacks. 

• Limited hardware capabilities: Edge devices often lack the computational power and memory to handle complex machine-learning models. 
• Difficulty in model updates: Compared to a centrally managed cloud-based system which supports easier and remote updates, it is challenging to update, and maintain machine learning models across multiple edge devices.
• Difficulty in accessibility: Edge AI systems may not provide the same level of universal access as cloud-based models.
• Potential need for fog computing: For complex applications, replicating cloud-like capabilities on regional edge servers (fog computing) may be necessary. It adds another layer of infrastructure and management complexity.

Security challenges and solutions for Edge AI

As we said, Edge AI avoids data leakage and privacy issues associated with transferring data to the cloud. But it also faces certain security challenges that must be properly addressed.

Challenges

• Privacy risks: Often, Edge AI devices handle very sensitive data. Therefore, they essentially need proper authorization settings. For example, medical applications should use appropriate encryption standards to comply with regulations like GDPR or HIPAA.
• Physical vulnerability: Edge devices are often deployed as individual gadgets. Devices like smartwatches or surveillance cameras are prone to tampering or theft, especially in public areas.
• Cybersecurity threats: As we discussed earlier, integration with 5G can bring new risks like man-in-the-middle attacks and eavesdropping.

(Explore the ethics of artificial intelligence.)

Solutions

Here are some solutions that can be implemented to avoid security issues with Edge AI.

• Implement secure boot and hardware security modules to prevent unauthorized firmware modifications. For example, industrial IoT devices can use HSMs to verify firmware integrity before operation.
• Use federated learning to address privacy and security challenges.
• Maintain encrypted communication protocols, especially for systems like smart homes and medical analysis.
• Apply regular software updates to protect against cyberattacks.

Developments coming up for Edge AI

With advancements in hardware design, such as energy-efficient AI chips and neuromorphic computing, edge devices will achieve unprecedented processing power while consuming minimal energy. As we already stated, 5G and IoT integration became a key advancement for Edge AI. In the future, integration with 6G networks and quantum computing is set to redefine the speed and scale of intelligent applications.

As we discussed, Edge AI already plays a central role in the evolution of autonomous systems. It enables robots, drones, and smart devices to operate with greater adaptability. In the future, it is expected to advance further, making these systems fully autonomous without human interaction. Also, in sectors like healthcare, personalized medicine powered by Edge AI will become a reality, which will provide real-time, patient-centric solutions.

Within a short span of the past 4-5 years, the Edge AI and its applications have boomed across several industries and applications. Further in the future, it will present prominent solutions for most of the global problems and reduce manpower even further with proper security features.