Feature: Embedded
malleable enough to handle the future allows innovators to deliver differentiating features to the market, instead of trying to predict what they may look like in advance. Powerful processing eases the job for both soſtware as well as test engineers, cutting the flab off cumbersome processes for leaner, more efficient teams. And, of course, having the capacity to build a more nuanced product, sensitive to consumer behaviour, based on hard, analytical data creates a more robust business and better future outcomes. Consumers always want more from
their products, and today that is in the health and wellness category, where there are many opportunities to provide empowering, actionable data that can improve product use and user lifestyle. Products of tomorrow require intelligent settings that adapt to consumer needs. So, what innovation zones do we see driving future platform developments?
AI’s impact on technologies Perhaps the most obvious innovation zone is using AI and Machine Learning (ML) to deliver novel features. Te ability to capture data throughout the development process is critical here. Even before a product is conceived, simply being able to envision an application for AI can help with product development, ensuring that the embedded platform is capable of delivering AI features later on – whatever they might be. By collecting real-world data, AI models
can be added to the platform and then used in an embedded device, such as a handheld consumer product. For example, a toothbrush could provide important insights about the pressure applied to gums, areas that may be missed when brushing, or length of time spent using it. Tis can then be used to train a model to adapt brush control based on unique consumer usage. Increasingly, we will see AI and ML
pushed into all areas of technology, not just in highly-sophisticated systems but embedded in peripherals, like sensors and accelerometers. Crucially, this will require a new way of thinking about system partitioning, with core functions moving out of the soſtware domain and into the hardware one. Tis affects how
Data collected from wearable smart fabrics can easily be read via a smartphone app for health and wellbeing
soſtware and hardware developers work together, too. Previously the two teams were separate, albeit working in tandem to develop a product. However, we will increasingly see these teams merge much sooner in the production phase, to prototype earlier in the development’s life cycle. Aſter all, holistic embedded technologies require holistic teams for effective development. Naturally, embedded developers will
require a change of mindset to facilitate this
shiſt. Firstly, this includes having a greater awareness of interfaces working with and being integrated into other systems. Expect to see AI engines increasingly
operating separately from the processor, too. A greater appreciation and understanding for what these new processing platforms can do for such systems, factored into the initial system architecture phase, is key for system development teams integrating AI and ML into their design flows.
www.electronicsworld.co.uk May 2024 23
Smart materials are just one application where data collection can help AI
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