What is 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. 

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 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 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

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.