AI-driven chip design involves the use of artificial intelligence (AI) technologies such as reinforcement learning, generative AI and AI agents in the tool flow to design, verify, and test semiconductor devices. For example, the solution space for finding the optimal power, performance, and area (PPA) for chips is quite large. There is a substantial number of input parameters that can be varied and lead to different results. Essentially, it is not humanly possible to explore all these combinations to find the best results in a given timeframe, which leaves some performance on the table.
AI can come up with the right set of parameters that delivers the highest ROI in a big solution space in the fastest possible time. In other words, better (and faster) quality of results than otherwise possible. By handling repetitive tasks in the chip development cycle, AI frees engineers to focus more of their time on enhancing chip quality and differentiation. For instance, tasks like design space exploration, verification coverage and regression analytics, and test program generation—each of which can be massive in scope and scale—can be managed quickly and efficiently by AI.
Your essential guide to overcoming AI chip complexity and achieving successful silicon outcomes from design to deployment.
AI-driven chip design solutions can take advantage of reinforcement learning, generative AI and AI agents to explore solution spaces and identify optimization targets.
Since most chip designs are based on the semiconductor manufacturer’s in-house, proprietary database, LLMs trained on open source, public data are generally not viable for leading-edge chip design. Instead, reinforcement learning (RL) offers a suitable solution for developing chip designs using proprietary databases where a pre-existing public dataset does not exist. Reinforcement learning learns optimal behavior in an environment, via interactions with the environment and observations of how it responds, to obtain maximum reward. The process involves learning as it goes, sort of a trial-and-error approach, including input from the user. As such, reinforcement learning generates better results over time.
Reinforcement learning is well-suited to electronic design automation (EDA) workloads based on its ability to holistically analyze complex problems, solving them with the speed that humans alone would be incapable of. Reinforcement learning algorithms can adapt and respond quickly to environmental changes, and they can learn in a continuous, dynamic way. RL has been used in chip designs for over 1,000 tapeouts across the industry and can now be considered a proven technology.
Another segment of AI that the semiconductor industry is using for chip development is generative AI. Based on large language models, generative AI learns the patterns and structure of input data and quickly generates content—text, videos, images, and audio, for example. Generative AI models have demonstrated their abilities in a variety of application areas, with the ChatGPT chatbot currently being one of the most publicly prominent examples.
In the EDA space, these chatbots typically take the form of copilots to assist the engineer in the design process. They can be used for initial code generation, documentation lookup, or ramping up junior engineers more quickly. For EDA, where chip design-related data is largely proprietary, generative AI holds potential for supporting more customized platforms or, perhaps, to enhance internal processes for greater productivity.
Another technology that is starting to be explored for chip development is AI agents. AI agents can automate mundane or repetitive tasks to free up the designer for more strategic and creative tasks. Furthermore, AI agents have the potential to work with each other in level one (L1) to level(L5) capability stages, where, ultimately, AI agents can be given full autonomy to make allowed decision by themselves.
By bringing together the powerful combination of greater intelligence and speed in tackling otherwise repetitive tasks, AI-driven chip design can generate better silicon outcomes and substantially enhanced engineering productivity. There are a variety of benefits of AI-driven chip design, including:
AI-driven chip design does come with some unique challenges. As a fairly new endeavor, being able to integrate AI technology into different chip design solutions requires new skills and behaviors, such as prompt engineering. There’s also a limited data set for AI training as much of the work being done in the industry is proprietary. Skepticism presents another challenge, as there are engineers who question how a machine could possibly derive better results than they can. With a talent shortage impacting the semiconductor industry, the industry will need to find those with the expertise and interest in optimizing EDA flows with AI technology, as well as in enhancing the compute platform for EDA algorithms.
AI workloads are massive, demanding a significant amount of bandwidth and processing power. As a result, AI chips – GPUs, XPUs, or dedicated accelerators designed to process AI workloads most efficiently – require a unique architecture consisting of the optimal processors, memory arrays, security, and real-time data connectivity. Traditional CPUs are ideal for performing sequential tasks but typically lag in the processing performance needed for accelerating AI workloads. GPUs, on the other hand, can handle the massive parallelism of AI’s multiply-accumulate functions and can be applied to AI applications. In fact, GPUs can serve as AI accelerators, enhancing performance for neural networks and similar workloads. For large data center and edge deployments, software-driven, custom-built ASICs are becoming increasingly popular for providing tailored performance with optimized power efficiency. As AI becomes mainstream, traditional CPUs are integrating AI-accelerating technologies, including features targeted for AI PCs.
Because of their complexity, AI chips will often break the reticle limit or be better served by partitioning the design into multiple die or chiplets. Multi-die architectures consisting of heterogeneous integration of multiple dies, or chiplets, in a single package are fast-becoming an ideal architecture for AI applications. Multi-die systems are an answer to the slowing of Moore’s law, providing advantages beyond what monolithic SoCs are capable of: accelerated, cost-effective scaling of system functionality with reduced risk and faster time to market.
AI accelerators are another type of chip optimized for AI workloads, which tend to require instantaneous responses. A high-performance parallel computation machine, an AI accelerator can be used in large-scale deployments such as data centers as well as space- and power-constrained applications such as edge AI.
GPUs, massively multicore scalar processors, and spatial accelerators are a few examples of hardware AI accelerators. These types of chips can be integrated into larger systems to process large neural networks. Some key advantages of AI accelerators include:
Regardless of the chosen architecture, AI-driven chip design technologies are streamlining the design process for AI chips, enabling better PPA and engineering productivity to get designs to market faster.
AI technologies are on track to become increasingly pervasive in EDA flows, enhancing the development of everything from monolithic SoCs to multi-die designs. They will continue to help deliver higher quality silicon chips with faster turnaround times. And there are many other steps in the chip development process that can be enhanced with AI.
While there are challenges in this space, with challenges come opportunities. By enhancing productivity and outcomes, AI can help fill the voids created by talent shortages as well as the knowledge gaps when seasoned engineers leave their roles. In addition, opportunities lie in exploring other ways in which AI can enhance chip design, including AI chips.
The energy impact of AI applications looms large. Yet, AI design tools can reduce its carbon footprint by optimizing AI processor chips (as well as the workflows to design, verify, and test the chips) for better energy efficiency.
Synopsys is a pioneer in pervasive intelligence, the application of interconnected, collaborative AI-powered EDA tools that span the complete silicon lifecycle, including architecture, design, verification, implementation, system validation, signoff, manufacturing, product test, and deployment in the field. Launched in March 2023, Synopsys.ai is the industry’s first full-stack, AI-driven EDA suite, empowering engineers to deliver the right chip with the right specs to the market faster. With continued enhancements to come, the suite currently includes:
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