Key takeaways
- The semiconductor industry is rapidly evolving to support AI applications across various sectors.
- Non-chipmaker companies are now producing specialised AI chips for specific tasks, indicating significant market crossover.
- Major semiconductor firms like Intel are heavily investing in R&D to develop advanced chips for consumer electronics.
- The industry's progress is closely linked to the impending deployment of 5G technology.
Semiconductors and artificial intelligence often go hand-in-hand. Leading enterprises, startups and even companies who don’t make chips have started manufacturing them for highly specific AI applications such as autonomous cars, virtual assistants and process automation. Now with several automotive companies and IT giants working toward getting self-driving cars on the road, semiconductor and automotive companies are extending their capabilities and crossing over to the chip or car market to develop their own self-driving cars, says a Frost research.
There is a crossover with non-chipmaker companies manufacturing specialised AI chips for specific applications and semiconductor players. For example, Intel capitalises billions of dollars in R&D programs every year to come up with more powerful chips which can influence the next generation of consumer electronics. “In fact, the semiconductor industry is closer to the deployment of the 5G technology than ever,” says an industry expert from Infiniti. According to another report, the global AI chipset market is expected to be worth $16.06 billion by 2022, growing at a CAGR of 62.9 percent between 2016 and 2022.
Semiconductor Players Are Making Smaller, Stronger Chips
Reportedly, Apple wants to build a self-driving car, while Tesla wants to build its own customised AI hardware. As of now, automotive applications have emerged as one of the major markets for AI chips, given the huge size of the transportation market itself. AI chips will play a central role in the success of the self-driving cars so that the cars can adapt to their environment and make driving decisions within a fraction of a second, while keeping the safety factor into consideration. AI chips will have to compute heavy amounts of data captured by systems such as LiDAR and other pictures or videos. The chips are expected to house several core GPU processors, along with accelerator chips for deep learning and computer vision accelerators.
