On-Device AI: Powering the Future of Computing

Key takeaways

• On-device AI is becoming increasingly popular as it makes its way onto wearable devices, smartphones, and smart home devices.

• Today, on-device AI plays a role in careers in AI research, hardware manufacturing, and mobile app development.

• The increasing popularity of AI applications is driving on-device AI updates across various devices, such as cars and smartwatches.

The role of on-device AI in wearables is to enhance their ability to track activity and monitor health data, such as heart rate and blood glucose levels. Read on to continue learning about on-device AI and best practices for implementing this growing technology.

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On-device AI updates

When Apple introduced the Siri feature in 2011, consumers and industry insiders considered the technology revolutionary. You could give your phone a voice command (e.g., to schedule a meeting with someone tomorrow at 10: 00 a.m.), and it would not only understand you but also access and interact with the appropriate apps to make it happen.

While Siri was one of the first examples of artificial intelligence (AI) on a mobile device, it won’t be the last. Indeed, since 2011, AI applications have become increasingly popular on smartphones and tablets and have even begun to appear on other devices, such as smartwatches and cars.

What does “on-device AI” mean?

On-device AI is artificial intelligence that processes data locally on a device rather than relying on cloud computing. On-device AI has several advantages over AI technologies that depend on the cloud, including the following:

• Increased privacy: By keeping data on the device, on-device AI protects users’ sensitive information from potential breaches during transmission.

• Faster processing: Local processing eliminates the delays in sending and receiving data from the cloud. In more technical terms, this is called having a lower latency. This lower latency enables faster responses and real-time decision-making, crucial for applications like voice recognition, chatbots, and autonomous systems.

• More personalization: When AI models run on a personal device, they can generate better responses by analyzing a user’s typical behaviors, unique speech patterns/facial expressions, and more. On-device AI can even respond to other data from the user or the user’s environment, such as analyzing your retail preferences or sensing your current health information.

Key concepts of on-device AI

When people discuss on-device AI, they often refer to related concepts. Edge computing and machine learning are two essential concepts commonly associated with on-device AI.

Edge computing

Edge computing involves processing data near its source, reducing the dependency on centralized cloud servers. Thus, edge computing and on-device AI are very closely related. Many people use the terms edge AI and on-device AI interchangeably. After all, on-device AI is the epitome of edge computing, bringing data processing to a user’s device and thus enhancing both speed and reliability.

Machine learning

Machine learning is a core component of AI. Some people even use the terms interchangeably. Technically, however, machine learning refers to the part of an AI system that allows the program to learn and adapt.

The machine learning models that programs employ require fewer resources because on-device AI runs on relatively small processors (compared to a cloud or data center, for example). These models include the following:

• Lightweight neural networks: Simplified versions of neural networks designed to perform specific tasks efficiently on limited hardware

• Decision trees: Compact models that excel in scenarios requiring quick decision-making and interpretable results with minimal data preprocessing

Read more: 10 Machine Learning Algorithms to Know

What are the key differences between cloud-based AI and on-device AI?

Cloud-based AI relies on cloud infrastructure to process and store data, while on-device AI does this directly from an edge or IoT (Internet of Things) device. These differences give each certain advantages over the other, depending on the use case. For example, cloud-based AI offers greater computing power, while on-device AI enables faster data transfer and greater security.

Applications of on-device artificial intelligence

On-device AI is becoming increasingly popular, and companies are just beginning to find ways to bring the technology to the market via smart devices, wearable devices, and automobiles. Consider how edge AI is being deployed in these areas.

• Smartphones and tablets: On-device AI revolutionizes user experiences on smartphones and tablets. AI models running locally can power features like voice recognition, image processing, and personalized recommendations. Besides personal assistants like Siri, Google’s AI photo editing tools are an example. Users can modify images in many ways (e.g., removing backgrounds, moving objects around, etc.), and the AI will fill in the rest of the photo to make it look natural.

• Wearable devices: Fitness trackers, smartwatches, and other wearable devices leverage on-device AI for health monitoring and activity tracking. These devices can analyze data like heart rate, steps taken, blood glucose levels, and sleep patterns in real time, providing users (and physicians) instant feedback and actionable insights.

• Smart home objects: On-device AI is pivotal in smart home devices like security cameras and thermostats. For instance, AI-enabled security cameras can detect motion, recognize faces, and instantly alert homeowners of unusual activity. Similarly, smart thermostats use on-device AI to learn user preferences and adjust settings for optimal energy efficiency.

• Automobiles: In the automotive sector, tasks like object detection, lane tracking, and navigation depend on AI models that process data in real time, as do virtual assistants that recognize voice commands. Many of the latest models also feature drowsiness detection systems that ask drivers to take a break if they detect swerving, maintenance predictors that alert owners before a breakdown, and “recommendation engines” that can suggest optimal settings based on driver patterns. As the industry moves more and more toward autonomous vehicles, on-device AI will become even more critical.

Careers associated with on-device AI

At this point, most people have begun using on-device AI as a consumer, at least to some extent. Of course, this trend also impacts the world of work as AI jobs become more popular. A few professions that use on-device AI extensively are as follows:

• Mobile app developers: Developers implement AI to create apps that respond to the user's preferences.

• Hardware manufacturers: Manufacturers are crucial in enabling on-device AI by designing and integrating specialized hardware components. These components, such as neural processing units (NPUs), enhance a device’s ability to run AI models effectively.

• AI researchers: Researchers continually explore new techniques for improving on-device AI. Their work focuses on enhancing AI results, making the process faster, and using even fewer computing resources. Another branch of AI research focuses on ethical concerns, ensuring that AI is safe and beneficial for humans.

Best practices for implementing on-device AI

If you are considering entering a field where you will be implementing on-device AI, you will have much to consider. A few elements that AI developers consider essential in creating on-device AI include the following:

• Model optimization: Producing high-quality results with limited computing resources will likely require advanced techniques, such as speculative decoding, quantization, compilation, pruning, distillation, and regularization.

• Data privacy: Enhanced data privacy is one of the most significant advantages of edge AI compared to using a cloud or data center. Maintaining that standard through data anonymization and access management strategies is essential.

• Testing and validation: Thorough testing and validation are crucial to ensuring the reliability and accuracy of on-device AI applications. Simulate as many real-world conditions as possible to ensure your program responds as expected.

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