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There¡¯s a lot of talk about AI these days and how it is transforming everything from the workforce to medicine, space exploration, and our lifestyles. AI is infiltrating all facets of technology. It¡¯s increasing the capacity of the data center to run vastly expanded workloads with greater levels of complexity more efficiently than ever before. It is simultaneously giving birth to computing at the edge and transforming the internet of things. But what exactly are AI chips and why are they so significant in our ability to advance to a new age?

Today¡¯s AI chips run AI technologies such as machine learning workloads on FPGAs, GPUs, and ASIC accelerators. They can handle many more variables and computational nuances, and they process exponentially more data than conventional processors. In fact, they are orders of magnitude faster and more efficient than traditional integrated circuits (ICs) for data-heavy applications.

There has been a revolution in semiconductor architecture to make AI chips happen. The latest advancement is to architect AI chips into many separate, heterogeneous components¡ªoptimized for their unique function¡ªin a single package. These multi-die systems break the limitations of traditional monolithic SoC designs which are fast reaching their performance ceiling. In fact, these multi-die systems are cornerstone in enabling deep learning capabilities. 

What is AI Chip Design?

Today, AI is not only being used as part of the functional work in chips, but it¡¯s also used to design chips, greatly enhancing engineering productivity. AI technologies are now integrated throughout the semiconductor development process to help design, verify, and test ICs. Chiefly, reinforcement learning enables fast identification of optimization targets. In addition, the industry is beginning to explore generative AI in chip design to better support customization and increase productivity. The benefits in using AI for chip design include:

• Significant improvements in power, performance, and area (PPA)
• The ability to reuse efficiencies achieved on (and learned from) other designs
• Higher productivity and faster time to market
• Faster design migration from node to node

These advantages are coupled with the ability of the technology to handle the tedium of iterative tasks, freeing engineers to focus on the design problems that will achieve competitive advantages. For more details check out our article on AI chip design.

AI Chips vs. Traditional Chips

The late Gordon Moore, past CEO of Intel, famously observed that on average the number of transistors on a chip (and thus performance) doubled every two years. This observation became known as Moore¡¯s law. As the years have passed and chips (and the process nodes they run on) have become ever-more sophisticated, the ceiling of Moore¡¯s law is closing in ¨C you simply cannot cram evermore transistors into ever smaller monolithic chips. There is a limit. In the last several years, the limit has been broken wide open by rethinking semiconductor architecture altogether. Today multi-die system architecture has paved the road for exponential increases in performance and a new world of design possibilities.

The Role of AI Accelerators in AI Chips

AI accelerators are one type of component die within a multi-die system. AI accelerators enable greater scalability and processing speeds of workloads. As machines, they are up to 1000x more energy efficient than general-purpose compute machines. This is critical, especially in the data center where a large portion of the power budget goes to simply keeping systems cool. It¡¯s also critical on the edge where low power is essential for the compute processing of connected devices. AI accelerators also lower the latency of the system. They not only enable scalability but also the heterogenous quality of the systems. For an in-depth exploration of what AI accelerators can do for your system, go to AI Accelerator. 

Power Management in AI Chips

Glitches are a power problem in AI chips. A glitch equates to unnecessary signaling happening in the system that can cause IR drops and electromechanical challenges. The impact can be minimal up to catastrophic. To guard against this, you need mechanisms in place to avoid, mitigate, and otherwise manage power glitches. Specifically, you need the correct delay data and the right tools to measure the power anomalies that lead to drastic boosts in power consumption. To avoid and mitigate glitches, it¡¯s essential to shift left in your design methodology. This has never been more essential than for AI chips whose processing capacity and power density is so much greater than traditional designs. Developing AI chips requires that you consider the optimal micro architecture early on to manage any glitches at the system and RTL levels. To learn the ins and outs of better managing power, read more at Glitch Power.

The Role of AI in EDA Tools and Verification

As with everything, AI is extending into EDA tools and verification. ²ÝÁñÉçÇø.ai? is the industry¡¯s first AI-driven, full-stack EDA design suite, facilitating all phases of design from architecture through signoff. It¡¯s revolutionary because it enables designers to:

• Scale the choices in design flows to achieve unbeatable PPA.
• Get the fastest time to their design targets by autonomously converging to tape-out-ready solutions.
• Scale their abilities to operate at expert levels.
• Verify their designs at higher coverage quality faster, so bugs don¡¯t go undetected.

You can learn more about the role of AI in EDA and verification tools at: How AI Enhances EDA Tools and Chip Design, Enhancing Chip Verification with AI and Machine Learning, and SoC Design Verification and Chip Debug with AI.

Use Cases for AI Chips

AI chips are ideal for hyperscale data centers. And the processing capability is beginning its march from the center to the edge, whether it¡¯s your smartphone, your smart house, or your smart anything. There is more data being created now than ever. AI chips play a key role in areas such as high-performance computing, where supercomputers crunch massive datasets that will help us better understand global warming, pandemics, and our place in the universe. In the data center, AI chips will help us reach new levels of efficiency, using less energy at the highest levels of performance. On the edge, AI chips will enable us to decentralize our processing power in a world where everything is truly connected. And, there are a lot of applications that haven¡¯t been born yet. We are only at the beginning of realizing what AI chips can do.

that the AI chip market was almost non-existent five years ago and that by 2021 it was $87 billion, and by 2030 analysts project it will break $1.6 trillion. The article notes that ¡°While most people are familiar with using  and  to distinguish between cats and dogs, emerging applications show how this capability can be used differently. Data prioritization and partitioning, for example, can be utilized to  optimize power and performance of a chip or system with no human intervention.¡±

The Future of AI Chips

While AI chips are already improving your experience with technology, you will most often see it today in areas that require the greatest levels of performance. In the future, photonics and multi-die systems will help to achieve greater power efficiencies, making the technology more accessible to more power-sensitive applications over time, becoming ever more pervasive. Read more about the future of AI in semiconductors in What Does the Future Hold for AI in Chip Design? and Top 2023 AI Predictions: AI Apps and Chip Design Tools.