Using AI to create better end products and services

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Hardware optimization. AI algorithms can optimize the allocation of SDH resources such as memory, logic blocks, and processor modules to achieve set goals and provide the ability to adapt as product requirements change. AI can also identify problems and deficiencies early in the design process through simulation and improve product reliability and safety. Hardware optimization can continue beyond the initial design stage, as edge processing allows engineers to dynamically manage resources to achieve set goals.

Accelerated testing: Deep learning surrogates now allow engineers to replace many physical tests with faster, less expensive virtual assessments.

With AI tackling these challenges, more companies will be able to create SDH for the first time or accelerate innovation. For example, London-based tech company Wayve is embedding its GenAI model LINGO-1 in the software of its self-driving cars to analyze driver input and data from the car’s sensors, including images azerbaijan mobile database on video. Using LINGO-1’s capabilities, the car’s software will generate answers to common questions like “Why are you stopping here?” Similarly, to improve autonomous driving, Waymo is using Carcraft, a program that simulates how virtual cars navigate different driving scenarios in real cities. The virtual cars travel nearly 8 million miles a day, much of it in complex driving situations like roundabouts.

The benefits of AI-enhanced SDH enable better products and services for end consumers. An example is the consumer experience provided by electric vehicle (EV) startups and traditional OEMs. New EV OEMs like Tesla, which were not constrained by legacy hardware, are using SDH and delivering over-the-air (OTA) updates to improve vehicle performance, efficiency, and features since 2018. This convenience is not possible with older, hardware-centric vehicles. Other OEMs are also investing in SDH or forming joint ventures to build it.