Beyond efficiency gains, SDH can help companies win new customers and increase brand loyalty through improved device performance and greater personalization. For example, a car’s infotainment system can deliver customized entertainment and streaming based on the driver’s previous choices. If SDH incorporates AI or machine learning algorithms, it can take performance to even greater heights and learn from user interactions.
As AI enables further reductions in product development costs, even more industries, including aerospace, medical devices, and consumer devices, can accelerate the scaling of SDH. However, they must first update their organizational structures and operations to take full advantage of AI. Here, we look at some of the changes that can help, with a focus on examples from the automotive industry.
For many companies, advances in AI come at an opportune time. Software complexity has been steadily increasing and could grow even more. Consider the automotive sector: Since 2021, the average automotive software platform complexity and the overall effort required to build it have increased by about 40% annually. However, software development productivity has only grown by 6% per year over the same period.
Using AI to Optimize SDH Design
Advances in AI are expanding product developers' austria mobile database and improving SDH creation by automating, optimizing, and improving various stages of design, development, and testing.
AI-assisted design. A number of generative AI (GenAI) tools can help engineers develop hardware specifications and designs, reducing manual work. For example, one tool optimizes the design of a hardware architecture based on given constraints and goals. Other new GenAI design systems can explore a much wider range of possible solutions than previous-generation tools. By comparing the results of thousands of simulations, they can help identify a design that delivers the most favorable combination of characteristics.
Software and hardware development. Using AI agents in the product development process can help bridge the gap between software and hardware design, ensuring that requirements are consistent across iterations. AI agents can also make hardware programming easier and optimize software for hardware performance (for example, AI can adjust software routines to make better use of GPUs, specialized accelerators, or other resources).