With the rise of highly optimized AI models that can run locally on your smartphone, on-device processing has recently received much attention. After all, being able to use the power of artificial intelligence without even having to connect to the internet is not only convenient, but also crucial to ensure security and confidentiality.
As a company providing scanning SDKs whose computer vision models process data entirely on the device, we have internalized the value of secure, privacy-focussed applications of artificial intelligence. Let us take a closer look at why you should prioritize on-device processing when choosing your software solutions.
What is on-device processing?
On-device processing refers to software performing computing tasks using only the resources available on the hardware it runs on. Applied to artificial intelligence, this means that the software does not need to connect to an external server or machine learning model.
Running computationally expensive tasks like natural language processing and image generation on less powerful hardware like smartphones can be facilitated by special chipsets. These often include an NPU (neural processing unit), also called an AI accelerator.
Processing data on the device reduces latency, which can lead to faster results. It also improves privacy, as the data never leaves the device.
The role of data privacy in AI
Privacy is the key reason behind the current movement toward on-device processing – or on-device intelligence, as it is often referred to in the context of AI applications.
When large language models (LLMs) exploded in popularity at the end of 2022, questions about the data these models were trained on and how they handled user input became a central issue. This was a major concern for businesses in particular, as they were eager to experiment with this new technology as part of their workflows, but deterred by the perceived lack of transparency in data processing.
Soon, enterprise tiers for popular AI tools emerged with the promise of not using user data to train their models. However, the problem of data traveling back and forth between the model and its users remained, as these transmissions can pose a security risk when not properly encrypted.
Fortunately, engineers were able to significantly reduce the size of machine learning models using techniques like neural architecture search and pruning. This eventually led to highly optimized models capable of running on mobile devices. Phone manufacturers are now implementing on-device intelligence on their flagship models, focusing on privacy in their advertising.
Still, some tasks require resources far exceeding even what phones with AI accelerators can provide. One approach is to differentiate between tasks that can be performed on the device and those that cannot. For the latter, the data is end-to-end-encrypted before it leaves the device and then transmitted to specialized, security-hardened server infrastructure. There, it is processed, sent back, and removed from the server node.
Until the next big leap in computing efficiency (of which DeepSeek-R1 might be a harbinger), companies will have to live with these kinds of compromises. But there are also applications of machine learning that have had plenty of time to mature and can be run on virtually any device.
Computer vision: a prime example of on-device intelligence
As a subfield of artificial intelligence, computer vision uses machine learning to extract meaningful information from visual inputs, allowing computers to recognize objects, detect patterns, and make decisions based on visual data. With research going as far back as the 1960s, this technology is now exceptionally refined and efficient, having found its way into self-driving cars and operating rooms.
Computer vision algorithms are a crucial part of the Scanbot SDK, allowing our customers to capture and extract information from barcodes, documents, and all kinds of structured data in seconds. Thanks to the technology’s high degree of maturity and computational efficiency, our customers can use the SDK’s functionalities without an internet connection, since all processing happens on the device. This is especially important for our clients in sensitive industries like healthcare, insurance, and finance.
We train our machine learning models in-house using only training data we created ourselves. Our clients then receive the improved models in a mobile-friendly size as part of the next SDK update – ready to be run locally on any supported device.
Conclusion
As artificial intelligence becomes more sophisticated, demand for on-device data processing will only increase. Running machine learning models and the applications that use them locally increases data privacy, enhances security, and reduces latency. If you are considering leaving the cloud behind, there has never been a better time.
