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The era of pervasive intelligence is being built with AI chips: highly specialized processors and accelerators designed to handle complex algorithms and enormous datasets.
But what does it take to build an AI chip that stands out in today’s fast-moving, highly competitive market?
The answer begins long before silicon is manufactured.
Navigate AI Chip Development
Your essential guide to overcoming AI chip complexity and achieving successful silicon outcomes from design to deployment.
The pre-silicon planning imperative
Designing an AI chip presents tremendous technical challenges. Developers must balance performance, power efficiency, scalability, and time-to-market — all while managing cost and risk. Pre-silicon planning is the phase where the blueprint for the entire journey is laid out, and key decisions are made that impact every subsequent step.
During pre-silicon planning, system requirements are defined, architectures are selected, and integration challenges are anticipated. This is not just a technical exercise — it’s a strategic one. Time and resources invested upfront can prevent costly mistakes later and help ensure the chip aligns with market and customer expectations.
Understanding the AI workload
One of the first tasks in pre-silicon planning is understanding the workload the AI chip is expected to handle. Workloads vary widely — from image recognition and large language models (LLMs) to autonomous driving and data analytics. Each use case demands a different balance of compute, memory bandwidth, and connectivity.
Developers analyze these workloads in detail, often using benchmarks and representative datasets. This enables accurate modeling of performance requirements and early identification of bottlenecks. For example, chips designed for training deep neural networks will have different needs compared to those optimized for inference in edge devices.
Architecture exploration and design choices
With workload requirements in hand, architecture exploration begins. Engineering teams consider whether to use a homogeneous array of processing elements or a heterogeneous mix of CPUs, GPUs, and custom accelerators. Memory hierarchy and interconnect fabric choices also play a crucial role in supporting target applications.
Advanced modeling tools like Synopsys Platform Architect are used to simulate different architectures and predict their behavior under realistic conditions. This enables engineering teams to make informed decisions and trade-offs between system architecture, performance, and power efficiency, ultimately saving time and reducing risk later in the design cycle.
Power efficiency: an end-to-end priority
Optimizing power efficiency is no longer an afterthought in AI chip design — it has become a critical, end-to-end consideration. AI workloads are dramatically reshaping the global energy landscape, with data centers worldwide projected to consume as much as 1,000 terawatt-hours (TWh) by 2026, which is equal to the energy consumption of entire nations. This surge in computational demand places immense pressure on engineers to design solutions that deliver exceptional performance while remaining energy efficient.
Semiconductor teams are increasingly adopting “shift left” methodologies to address this challenge. By prioritizing power optimization from the very start of the design cycle, engineers can proactively evaluate architectural choices, model power consumption, and implement strategies that minimize energy use. Early and continuous focus on power efficiency enables impactful decisions throughout the design process — helping meet sustainability goals, manage operational costs, and deliver chips that are ready for the demands of next-generation AI applications.
IP selection and integration
Modern AI chips are rarely built from scratch. Instead, they integrate proven intellectual property (IP) blocks — such as processor cores, memory controllers, and connectivity interfaces — into the design. Selection and integration of IP is a critical step in pre-silicon planning.
Our broad portfolio of silicon-proven IP is designed for interoperability and optimized for all major foundry technologies, helping accelerate integration and verification while minimizing risk.
The future of AI chip design
As AI continues to evolve, so do the challenges of chip design. Advanced process nodes, 3DIC architectures, and new types of accelerators are pushing the boundaries of what’s possible. Pre-silicon planning remains the most powerful method for navigating this complexity and delivering innovative solutions.
Investing in robust pre-silicon planning is key to building AI chips that drive the next wave of technological breakthroughs. By combining deep workload analysis, comprehensive architectural exploration, power optimization, and silicon-proven IP, the stage is set for success — long before the first wafer is ever produced.
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