AI technologies and tasks require specialized AI chips that are more powerful, more efficient, and optimized for advanced machine learning (ML) algorithms. Image recognition, recommendation engines, natural language processing (NLP), and autonomous vehicles are just some of the use cases that these chips support.
Some of the biggest names are involved in the development of AI chips, including traditional players like Intel, Samsung, Broadcom, and Qualcomm, with major investment going into the development of this technology. Graphics processing units (GPUs) have been favored for AI applications due to their ability to perform millions of mathematical operations in parallel, with AMD and Nvidia being notable players.
In recent years, AI chip startups have emerged as they look to disrupt the market, all of whom have received major funding to support their activities. In addition to these startups, larger tech firms like Apple and Google are also looking to innovate in the AI chips space, with the latter producing the Tensor Processing Unit (TPU) – an application-specific integrated circuit (ASIC) developed specifically for AI activities.
Experts suggest AI chips will find their way into an increasing number of consumer devices, such as smartphones, tablets, smart speakers, and wearables. In addition to this, they will also be used in multiple enterprise markets, such as robotics, sensors, and other IoT (internet of things) devices, with further growth expected as smart city initiatives take hold.