Google’s AI Can Design Computer Chips In Under 6 Hours

Navin Bondade 0 Comments Artificial Intelligence, Data Science, Deep Learning, Machine Learning

In a recently published paper lead by Jeff Dean, scientists at Google Research and the Google chip implementation and infrastructure team described an AI technology that can design computer chips in less than six hours which is significantly faster than the weeks it takes human experts in the loop.

Google said that the AI system follows a learning-based approach to chip design that can learn from past experience and improve over time, becoming better at generating architectures for unseen components.

According to the company, the new technology advances the state of the art in that it implies the placement of on-chip transistors can be largely automated. If made publicly available, the Google researchers’ technique could enable cash-strapped startups to develop their chips for AI and other specialized purposes.

Moreover, it could help to shorten the chip design cycle to allow hardware to better adapt to rapidly evolving research. The AI system is built upon a technique proposed by Google in a paper published in March.

“Basically, right now in the design process, you have design tools that can help do some layout, but you have human placement and routing experts work with those design tools to kind of iterate many, many times over.”

Dean told in an interview late last year. “It’s a multi-week process to actually go from the design you want to actually have it physically laid out on a chip with the right constraints in area and power and wire length and meeting all the design roles or whatever fabrication process you’re doing”.

Dean further said, “We can essentially have a machine learning model that learns to play the game of [component] placement for a particular chip.”

Explaining the process, the blog post stated in essence, the approach aims to place a “netlist” graph of logic gates, memory, and more onto a chip canvas, such that the design optimizes power, performance, and area (PPA) while adhering to constraints on placement density and routing congestion.

The graphs range in size from millions to billions of nodes grouped in thousands of clusters, and typically, evaluating the target metrics takes from hours to over a day. The researchers devised a framework that directs an agent trained through reinforcement learning to optimize chip placements.

Given the netlist, the ID of the current node to be placed, and the metadata of the netlist and the semiconductor technology, a policy AI model outputs a probability distribution over available placement locations, while a value model estimates the expected reward for the current placement.

While testing the team started with an empty chip, the abovementioned agent as mentioned above places components sequentially until it completes the netlist and doesn’t receive a reward until the end when a negative weighted sum of proxy wavelength and congestion is tabulated.

To guide the agent in selecting which components to place first, components are sorted by descending size placing larger components first reduce the chance there’s no feasible placement for it later.

According to the team, training the agent required creating a data set of 10,000 chip placements, where the input is the state associated with the given placement, and the label is the reward for the placement.

To build it, the researchers first picked five different chip netlists, to which an AI algorithm was applied to create 2,000 diverse placements for each netlist.

Post testing, the co-authors report that as they trained the framework on more chips, they were able to speed up the training process and generate high-quality results faster. In fact, they claim it achieved superior PPA on in-production Google tensor processing units as compared with leading baselines.

According to the researchers, “Unlike existing methods that optimize the placement for each new chip from scratch, our work leverages knowledge gained from placing prior chips to become better over time.”

Additionally, “our method enables direct optimization of the target metrics, such as wavelength, density, and congestion, without having to define … approximations of those functions as is done in other approaches.

Not only does our formulation make it easy to incorporate new cost functions as they become available, but it also allows us to weigh their relative importance according to the needs of a given chip block (e.g., timing-critical or power-constrained),” concluded the researchers.