Apple Team Shared The Secret On How They Stay Ahead Of The Competition With A13 Chip

Aadhya Khatri


Phil Schiller and Anand Shimpi, two employees of Apple, shared their approach in the design of chips and how they improve the performance every year

Recently, Phil Schiller, Apple’s VP of product marketing, and Anand Shimpi, a member of the Platform Architecture team, shared their approach in the design of chips and how they manage to improve their performance every year.

In their interview, they pointed out how they made the chip to become more power efficient and what users can gain from Apple’s effort.

In their interview, they pointed out how they made the chip to become more power efficient and what users can gain from Apple’s effort

During the announcing event earlier this month, Apple shared some details regarding its A13 Bionic chip. It sports 8.5 billion transistors, two performance-focused cores, four efficiency-focused ones, making up a total of six cores, two machine learning accelerators, a quad-core GPU, and an octa-core neural engine. So the chip can work on a trillion operations each second.

So in comparison with the A12, the A13’s performance is 20% faster and its efficiency is increased by 30%. Many of Apple’s competitors now have eight cores in their chips, the iPhone maker’s smooth integration of software and hardware gives it a competitive edge over them all.

According to Anand Shimpi and Phil Schiller, their focus was efficiency when they develop the chip:

To shed some more light on the process of developing the chip, Schiller and Shimpi shared that CPU designs are guided by specific applications.

The A13 Bionic chip features six cores in total

Apps that do not require further optimization will use less energy than usual. The interview also uncovered that the iPhone maker makes use of the same approach to develop its machine learning and GPU.

The fact that the A13 has a different way of processing has made it stand out from other chips on the market.

You can imagine the A13 to have one single home basis but with the usual on-and-off approach, which can help reduce the rate of electrodes going waste.

Schiller said that the key for all these optimizations was machine learning:

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