Samsung has its own AI-designed chip. Soon others will too

Samsung uses artificial intelligence to automate the incredibly complex and subtle process of designing advanced computers fries.

The South Korean giant is one of the first chipmakers to use AI to create its chips. Samsung uses AI features in new software Synopsis, a leading chip design software company used by many companies. “What you see here is the first of a true business processor design with AI,” says Aart de Geus, president and co-CEO of Synopsys.

Others including Google and Nvidia, talked about chip design with AI. But Synopsys’ tool, called, may prove to be the most comprehensive because Synopsys works with dozens of companies. The tool has the potential to accelerate semiconductor development and unlock new chip designs, according to industry watchers.

Synopsys has another valuable asset in manufacturing AI-designed chips: years of advanced semiconductor design that can be used to train an AI algorithm.

Samsung spokesperson confirms company is using Synopsys AI software to design its Exynos chips, which are used in smartphones, including its own branded handsets, as well as other gadgets. Samsung has unveiled its all-new smartphone, a foldable device called the Galaxy z fold3, earlier this week. The company has not confirmed whether the AI-designed chips have yet entered production, or in what products they might appear.

Across the industry, AI seems to be changing the way chips are made.

A Google search document published in June describes the use of AI to organize components on the Tensor chips which it uses to train and run AI programs in its data centers. Google’s next smartphone, the Pixel 6, will feature a custom chip made by Samsung. A Google spokesperson declined to say whether AI helped design the smartphone’s chip.

Chip manufacturers, including Nvidia and IBM are equally get into the design of AI-driven chips. Other manufacturers of chip design software, including Cadence, a competitor of Synopsys, are also develop AI tools to help draw plans for a new chip.

Mike demler, a senior analyst at the Linley Group who tracks chip design software, says artificial intelligence is well suited for organizing billions of transistors on a chip. “It lends itself to these problems which have become extremely complex,” he says. “It will just become a standard part of the calculation toolbox. “

Using AI tends to be expensive, Demler explains, because it requires a lot of cloud computing power to train a powerful algorithm. But he expects it to become more accessible as the computational cost drops and models become more efficient. He adds that many of the tasks involved in chip design cannot be automated, so expert designers are always needed.

Modern microprocessors are incredibly complex and have multiple components that must be combined efficiently. Sketching out a new chip design normally requires weeks of painstaking effort as well as decades of experience. The best chip designers use an instinctive understanding of how different decisions will affect each step of the design process. This understanding cannot be easily written into computer code, but some of the same skills can be captured using machine learning.

The AI ​​approach used by Synopsys, along with Google, Nvidia, and IBM, uses a machine learning technique called reinforcement learning to craft the design of a chip. Reinforcement learning involves train an algorithm to perform a task by reward or punishment, and it has proven to be an effective means of capturing subtle human judgment that is difficult to codify.

The method can automatically define the basics of a design, including the placement of components and how to connect them together, trying out different designs in simulation and learning which ones produce the best results. This can speed up the process of designing a chip and allow an engineer to experiment with new designs more efficiently. In June blog post, Synopsys said a North American integrated circuit maker improved a chip’s performance by 15% using the software.

Most famous, reinforcement learning has been used by DeepMind, a subsidiary of Google, in 2016 to develop AlphaGo, a program capable of mastering the board game Go well enough to defeat a world-class Go player.

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