The dawn of on-device AI processing

/ By Deon Van Zyl /

Artificial intelligence (AI) has become integral to our daily lives, from voice assistants to generative text and images. However, the future of AI lies in on-device processing, a technology that brings AI capabilities directly to our devices and transforms how we interact with technology.

The Importance of On-Device AI Processing

On-device AI processing involves running AI algorithms directly on devices such as smartphones and laptops, instead of relying on cloud servers. As device processors become more powerful simultaneously, the on-device AI is expected to offer increasingly sophisticated and personalized user experiences. On-device AI processing enhances privacy, reduces latency, improves performance, enables offline functionality, and strengthens security by minimizing the attack surface. Also, the battery life is saved by the reduction of cloud communication.

One of the most exciting developments is the ability to run large language models (LLMs) locally. This capability can enhance applications like virtual assistants and translation services by providing faster and more reliable responses. On-device AI processing significantly increases response times because it does not need to communicate with remote servers. This is essential for applications requiring real-time feedback like augmented reality (AR) and gaming. Furthermore, it is particularly crucial for applications that handle personal data for example health trackers and financial apps, to process and store this data on-device. This guarantees that sensitive information remains on the user's device reducing the risk of data breaches and enhancing privacy.

The Essential Components

The key components that make up on-device AI processing are specialized hardware, optimized software, and advanced machine learning models.

Specialized Hardware

Modern devices are equipped with AIspecific hardware for example neural processing units (NPUs), digital signal processors (DSPs), and graphical processing units (GPUs). These components are aimed to handle the parallel processing required for AI tasks. Specifically looking at the NPU area. Qualcomm’s latest processors (Snapdragon 8 Gen 3) leverage multiple processors. Including the function of a central processing unit (CPU) and graphics processing unit (GPU).

Optimized Software

Software frameworks and libraries have been optimized to run AI models on limitedresource environments. These frameworks leverage the specialized hardware to deliver high performance without draining battery life excessively.

Advanced Machine Learning Models

Recent advancements in machine learning have made it feasible to deploy AI on devices. These models are designed to be both accurate and efficient, allowing them to run effectively on smaller hardware footprints.

The AI-Driven Devices Poised to be Revolutionize

Smartphones are at the forefront of this revolution. Manufacturers like Apple and Google integrating AI capabilities directly into their latest models. Features like improved real-time photo and video editing, voice recognition and can now be found on them. It is not just smartphones, as Microsoft has also announced their ARM-powered Surface laptop, which uses Qualcomm’s Snapdragon X. These laptops are considered a Copilot+ PC, which therefore has a lot of additional functionality such as Cocreator, Live captions, and Recall. Microsoft's new "Recall" feature for Windows 11 is created to record everything you do on your PC while providing a comprehensive log of user activities. This feature creates a detailed semantic index of your actions which allows you to search for and retrieve past activities using natural language queries. For instance, you can search for specific content across various applications like PowerPoint, Excel, and messaging apps. Making it easier to find and use historical information. This generated data is stored securely with BitLocker encryption. Users can manage their privacy settings by controlling which applications are recorded and even deleting specific snapshots or entire time ranges of recorded activities.

Local AI-Driven Actions

AI running locally on a device has the capability for users to state specific tasks and execute the required steps to achieve them. Examples of the tasks could be changing the screen brightness, managing files, controlling hardware, or even more complex multiple-step processes. Two notable advancements in device control via on-device AI are operating system APIs and the grounding technique from xLang.ai. Now take this scenario, where a user wants to troubleshoot the computers or capture scanned receipts into Excel. The AI identifies the necessary steps to achieve a user-stated goal within a device or installed software. After this, it can determine how to interact with the operating system or applications to perform these actions on the user’s behalf. Possibly, by executing generated scripts or manipulating the mouse and keyboard. When actioning the steps, AI can check if the steps are successful and proceed until the goal is achieved. As OnDevice AI Processing continues to develop, it will enable more powerful and personalized AI applications while transforming our digital experiences. Therefore, the future of AI is not only in the cloud but also in the palm of our hands, and the possibilities are limitless.

Deon van Zyl (Norway)

BCom (Hons), Senior System Developer

Linkedin: deonvanzyl

Deon is a sophisticated technical IT professional with a solid history of effectively bridging the gap between Programming, Security, Digital Forensics, Artificial Intelligence, and Teaching. His track record of over 25 years, has a footprint that spans major corporations, academic institutions, and government.