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Definition
AI-driven chip design involves the use of artificial intelligence (AI) technologies such as machine learning 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¡ªeach of which can be massive in scope and scale¡ªcan be managed quickly and efficiently by AI.
How Does AI Chip Design Work?
Today¡¯s AI chip design solutions typically use reinforcement learning to explore solution spaces and identify optimization targets. The science of decision making, 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. As such, reinforcement learning generates better results over time.
Reinforcement learning is 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.
Another segment of AI that the semiconductor industry is starting to explore 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¡ªtext, 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.
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.
The Benefits of AI Chip Design
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 chip design, including:
- Enhanced PPA. In every chip design, there are opportunities to deliver the optimal PPA for its target application. However, with an almost infinite number of design choices in massive design spaces, it is humanly impossible to find the right choices within a project¡¯s timeframe. AI can enhance PPA by taking on exploration of these large design spaces to identify areas for optimization.
- Enhanced Productivity. Engineers consistently report heavy workloads in an environment of shrinking resources and talent shortages. By handling iterative tasks, AI frees engineers to focus on chip design differentiation and quality while meeting time-to-market targets.
- Support for Reuse. Since learnings from one project can be retained and applied to the next, AI drives even greater efficiencies into chip development processes.
- Faster Design Migration. With the support of AI, chip design teams can more quickly migrate their designs from one process node to another.
What are the Key Challenges in AI Chip Design?
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 an in-depth understanding. 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.
There¡¯s also a limited data set for AI training. 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.
AI Chips vs. Traditional CPUs and GPUs
AI workloads are massive, demanding a significant amount of bandwidth and processing power. As a result, AI chips require a unique architecture consisting of the optimal processors, memory arrays, security, and real-time data connectivity. Traditional CPUs typically lack the processing performance needed, but are ideal for performing sequential tasks. 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.
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 as well. 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.
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 Accelerators and AI Chip Design
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:
- Energy efficiency compared to general-purpose compute machines
- Lower latency plus fast computational speed
- Scalability
- Heterogeneous architecture, which can accommodate multiple specialized processors for specific tasks
What is the Future of AI Chip Design?
AI technologies are on track to become increasingly pervasive in EDA flows, enhancing the development of everything from monolithic SoCs to multi-die systems. 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.
AI Chip Design and Synopsys
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:
- Synopsys DSO.ai?, which autonomously searches for optimization targets in a chip design¡¯s very large solution spaces
- Synopsys VSO.ai, which autonomously achieves faster verification coverage closure and regression analysis for faster functional testing closure, higher coverage, and predictive bug detection
- Synopsys TSO.ai, which automatically searches for optimal solutions in large test search spaces to minimize pattern count and automatic test pattern generation (ATPG) turnaround time
Synopsys.ai: AI-Driven EDA
Optimize silicon performance, accelerate chip design and improve efficiency throughout the entire EDA flow with our advanced suite of AI-driven solutions.
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