This blog post was originally published at Synopsys’s website. It is reprinted here with the permission of Synopsys.
As the saying goes, with great risk comes great reward.
Because of the profound complexity and intricate dependencies between hardware and software, developing a custom AI chip is one of the most capital-intensive and high-risk endeavors in the semiconductor industry today.
The total project cost can easily exceed $100 million on advanced process nodes. Those costs rise dramatically if the design has to go back to the foundry for a silicon respin. And the worst-case scenarios — missed funding opportunities, time-to-market delays, and the resulting loss of market share — can be catastrophic.
And yet, an increasing number of chipmakers and startups are undaunted by these risks because of the significant rewards they stand to gain. But there is little margin for error, and getting it right the first time has become a technical, financial, and business imperative.
Here are ten proven strategies for achieving first-pass silicon success when developing AI chips:
1. Prioritize early architecture exploration
Optimizing the architecture of an AI chip from the outset pays big dividends. Early architecture exploration — using advanced modeling and simulation tools like Synopsys Platform Architect — allows teams to evaluate multiple configurations and tradeoffs for compute, memory, and interconnects. Performance and power can be optimized for specific AI workloads. And because most AI chips are multi-die designs, specialized tools are available for analyzing and optimizing the partitioning and configuration of individual chiplets within the overall package. By prioritizing early architecture exploration, teams can quickly identify potential bottlenecks and optimize their design for complex algorithms and massive AI datasets.
2. Leverage silicon-proven IP solutions
Using silicon-proven IP solutions — for high-speed interfaces, memory controllers, specialized accelerators, and security — reduces risk and speeds up development. Synopsys offers the industry’s broadest IP portfolio, with solutions that are silicon-proven across all major foundries and advanced process nodes, pre-verified with PHY, controller, and verification IP, and compliant with leading industry standards. Together, these attributes promote ecosystem interoperability and reliability, reduce integration challenges, and help design teams focus on differentiating features.
3. Design software and hardware in tandem
AI chips do not operate in isolation — they are part of a larger system that includes software, firmware, and external components. Engaging software teams in pre-silicon planning helps ensure hardware is tailored to the algorithms and frameworks the AI chip will support. And by providing hardware models that allow the actual software to be run before silicon is available, teams can validate functionality, optimize for real-world performance, and identify bugs or integration challenges before they impact product quality or delivery.
4. Optimize power efficiency throughout the development process
AI workloads are placing a massive strain on global energy resources and infrastructure. While power efficiency used to be an afterthought in chip design, it has become a critical, end-to-end priority that influences the commercial viability, differentiation, and sustainability of AI chips. Power modeling helps teams make informed architecture decisions that minimize energy consumption without impacting performance targets. And according to Synopsys customer surveys, focusing on power during early architecture exploration and RTL can have a far greater impact on efficiency than attempting to address it in the latter stages of design.
5. Conduct rigorous RTL design verification
A robust RTL design verification strategy combines simulation, formal verification, and coverage-driven methodologies to ensure both functional and performance requirements are met. Early and continuous verification — using AI-powered, industry-standard flows — helps minimize the risk of undetected bugs and late-stage surprises. Adding RTL emulation with hardware-assisted verification (HAV) tools like Synopsys ZeBu allows teams to validate system behavior, software integration, and performance under real-world conditions. Emulation also catches functional bugs, corner cases, and integration issues that may be missed by simulation alone, reducing the risk of costly silicon respins.
6. Utilize AI-powered EDA tools to boost productivity and optimize PPA
AI-powered EDA tools bring recursive learning (RL), copilot assistance, and data analytics into the design process, automating routine tasks and uncovering design optimization opportunities. These tools can rapidly evaluate thousands of design alternatives, helping teams accelerate design cycles, reduce manual optimization efforts, and achieve aggressive power, performance, and area (PPA) targets.
7. Design and simulate advanced packaging
AI chips typically require advanced packaging solutions, such as 2.5D and 3D multi-die architectures. Because different chiplets from different process nodes can be mixed and matched within the same package, these architectures can increase flexibility, reduce cost, and help meet performance requirements. But optimizing functionality and avoiding late-stage surprises requires early co-design, simulation, and analysis of the packaging system, including signal integrity and thermal analysis. Close collaboration between silicon and packaging teams helps ensure all aspects — from interconnects to heat dissipation — are optimized for performance, overall cost, and first-pass success.
8. Conduct extensive pre-silicon system validation
Before sending designs to the fab, extensive hardware-assisted emulation, prototyping, and system validation — using platforms like Synopsys HAPS — are essential. Implementing comprehensive test plans, real-world interface validation boards, and quick turnaround software environments with advanced debug capabilities increases the likelihood of first-pass silicon success and accelerates bring-up and debugging when first silicon arrives. For leading-edge AI chip designs, achieving this level of readiness requires quadrillions of verification and validation cycles.
9. Implement test and lifecycle management capabilities
Testing strategies should be implemented well before first silicon arrives, especially when developing multi-die designs. Design for test (DFT) capabilities — such as scan chains and built-in self-test (BIST) — improve fault detection and isolation. They provide valuable insights that help increase yield, reliability, and performance. And when paired with silicon lifecycle management (SLM) solutions, they enable monitoring and optimization throughout the chip’s lifespan.
10. Choose external collaborators early and wisely
Early and ongoing engagement with external collaborators, including semiconductor foundries and technology partners, is vital for project success. Foundries offer critical guidance on process capabilities and manufacturability. Technology partners like Synopsys provide essential design tools, silicon-proven IP, and collaboration-driven design services. And both facilitate access to the broader ecosystem of hardware and software with which the AI chip will interact. Working with these partners helps augment in-house resources and expertise, allows greater focus on core competencies and innovation, and ultimately improves design quality and time-to-market. Establishing these relationships early and collaborating throughout the design cycle is essential for optimizing an AI chip’s performance, yield, and reliability. And it helps ensure a seamless transition from tapeout to proof-of-concept or mass production ramp.
Achieving first-pass silicon success
In the high-stakes world of AI chip development, the margin for error is razor thin. With investments often exceeding $100 million and the potential for costly delays looming over every decision, delivering a successful design on the first attempt is essential.
Adopting a disciplined, proactive approach — prioritizing early architecture exploration, silicon-proven IP, AI-driven EDA tools, extensive hardware-assisted verification, early software development, robust DFT and SLM practices, and strong collaboration across teams — helps engineering organizations minimize risk and drive breakthrough performance, on schedule and on budget. With Synopsys as a trusted partner, AI chip companies can greatly improve their chances of first-pass silicon success.
Frank Schirrmeister, Executive Director, Strategic Programs, System Solutions, Synopsys
Rita Horner, Product Marketing Director, Synopsys
Todd Koelling, Senior Director, Product and Solutions Marketing, Synopsys
