Apple iPhone 16 Pro may get enhanced on-device AI capabilities with new A18 Pro chip

tech.hindustantimes.com:

Apple is gearing up for a significant upgrade in the realm of artificial intelligence (AI) with its upcoming iPhone 16 Pro. According to insights shared by analyst Jeff Pu from Haitong International Tech Research, Apple is set to introduce substantial modifications to its A18 Pro chip, specifically aimed at enhancing on-device AI capabilities. This revelation has sparked anticipation among tech enthusiasts as Apple accelerates production of the A18 Pro chip well ahead of its usual schedule.

Pu's investor note, renowned for its accuracy in predicting Apple's chip developments, unveils intriguing details. It highlights a surge in demand for Apple's A18 chip, signalling a notable shift from its predecessor, the A17 Pro. Notably, the A18 Pro is anticipated to feature a larger die area, a strategic move that underscores Apple's commitment to edge AI computing, 9to5Mac reported.

Also read: Apple in talks to use Baidu generative AI in Chinese iPhones, Wall Street Journal says

Expanding the die area of the chip signifies a capacity for accommodating more transistors and specialized components, thus potentially amplifying performance. However, it also introduces challenges such as heightened risks of defects and energy efficiency concerns. Apple faces the delicate task of striking a balance as it intensifies production of the A18 Pro chip, laying the groundwork for the iPhone 16's debut later this year.

The concept of edge AI computing takes centre stage in Apple's strategic roadmap, heralding a paradigm shift in AI processing. While some AI features may rely on cloud infrastructure, collaborations with tech giants like Google are rumoured, and Apple is poised to harness the power of on-device AI processing extensively.

Also read: Samsung Galaxy A35 review: Amazing design, display and battery, but does it tick all the boxes? Find out

Edge AI computing leverages the synergy between edge computing and AI, enabling the execution of machine learning tasks directly on interconnected