What is AI Chip Design?

There are several types of AI chips, including graphics processing units (GPUs) with AI-specific cores, CPUs enhanced with AI capabilities, custom-designed application-specific integrated circuits (ASICs), neural processing units (NPUs), and other architectures purpose-built for AI. Each of these chips is tailored to deliver the best performance, power efficiency, and scalability for different AI applications, ranging from massive data center deployments to power-sensitive edge devices.

As AI technology rapidly evolves, AI chips have become the backbone of innovations in fields such as natural language processing, computer vision, advanced physics and medical simulations, autonomous vehicles, robotics, and more. Their specialized architecture enables faster training and inference of AI models, making real-time, intelligent decision-making possible across industries. Designing these chips requires a holistic approach across silicon, packaging and software making the best use of advanced design techniques to achieve performance and power targets in the quickest development time possible while enabling high end-deployment reliability, availability and serviceability (RAS) across the system lifecyle.

The rapid increase in demand for AI chips is closely linked to the explosive growth of generative AI and large language models (LLMs). Over the past decade, AI models have increased in size and complexity, requiring more computational power and memory than ever before. Generative AI models, such as GPT-5, DALL-E 3, and Llama 4 have set new benchmarks in both training and inference demands. This surge in compute requirements has driven a corresponding surge in projected data center energy requirements¹ and driven the need for new chip architectures capable of supporting the vast scale and speed required by these breakthroughs while reducing chip power usage.

Compute and Accelerator Cores: AI chips are equipped with numerous parallel compute or accelerator cores, such as tensor cores or matrix multipliers, that can handle the large-scale mathematical operations found in neural networks. GPUs, for example, may contain thousands of cores optimized for parallel processing, while custom ASICs and NPUs are designed for even greater specialization.

Memory Bandwidth: AI workloads often require moving large amounts of data quickly. AI chips typically integrate high-bandwidth memory (HBM) to ensure that compute cores have fast access to data, minimizing bottlenecks and maximizing throughput.

Interfaces and Networking: Die-to-die interconnect can be realized inside the AI chip package using technologies like UCIe. Advanced interconnect protocols between AI chips like NVLink Fusion and UALink and chip-to-chip and networking like PCIe 7.0/8.0, 224G/448G Ethernet, Ultra Ethernet and Compute Express Link (CXL) are used to connect AI chips to each other and system components, enabling rapid data exchange in high-performance environments like AI factories and data centers.

• GPUs: Originally designed for graphics rendering, GPUs have become central to AI due to their high parallelism and support for deep learning operations. Modern GPUs feature dedicated AI cores, such as tensor cores and matrix cores, to boost AI performance.
• Custom AI Accelerators: Many hyperscalers and startups are building custom ASICs and established semiconductor companies are creating specialized processors to push the limits of AI speed while improving carbon-efficiency².
• NPUs and Edge AI Chips: Neural Processing Units (NPUs) and other edge AI chips are optimized for efficient inference in low-power environments, such as smartphones, IoT devices, and autonomous vehicles.
• FPGAs: Field programmable gate arrays (FPGAs) are fully programmable chips that can be configured – or reconfigured – as the name implies.  FPGAs can be used to accelerate an algorithm or portion of an AI algorithm in hardware rather than software, resulting in power savings.  FPGAs are programmed using a hardware description language (HDL) such as VHDL or Verilog. Because they are off-the-shelf silicon components, FPGAs typically have lower non-recurring engineering (NRE) costs than full-custom ASICs. However, because they come in fixed increments of logic elements (LEs), logic cells (LCs) or look-up tables (LUTs), some of those resources (gates) may go unused resulting in higher power and non-optimized costs than full custom designs.  Hence, FPGAs typically make the most sense for lower-volume, specialized designs where the economies of scale of ASICs do not come into play or reconfigurability is necessary.

AI chips deliver several key advantages over traditional processors, making them essential for modern AI deployments:

• Superior Performance: Purpose-built architectures enable AI chips to process complex models and massive datasets faster than CPUs or general-purpose GPUs.
• Energy Efficiency: AI chips are optimized to minimize power consumption, making them ideal for both large-scale data centers and power-sensitive edge devices.
• Scalability: Advanced interface and networking capabilities allow AI chips to be deployed in large clusters, supporting the training and inference of the largest AI models.
• Flexibility: Many AI chips support a wide range of AI frameworks and model types, making them suitable for diverse applications—from natural language processing and computer vision to recommendation systems and robotics.
• Lower Total Cost of Ownership (TCO): Enhanced performance and energy efficiency help organizations reduce operational costs and maximize return on investment in AI infrastructure.
• Future-Readiness: AI chips are designed to support the latest advancements in AI, ensuring that systems can keep pace with rapid innovations in the field.

Developing successful AI chips requires careful attention to a range of technical and strategic factors across the entire silicon development process and lifecycle. As AI workloads grow in complexity and scale, under compressed design cycles, engineering teams must address unique design, verification, and manufacturing challenges to ensure first-pass silicon success and competitive differentiation.

• Architecture Exploration: Early architectural exploration is critical. Teams must balance compute, memory, and interconnect resources to meet demanding AI model requirements while optimizing for performance, power, and area (PPA). Selecting the right process technology, partitioning workloads efficiently, and leveraging advanced packaging (such as 2.5D and 3D integration) are foundational for scalable, high-performance AI chips.
• Developing Silicon, Package, and Software in Parallel: Accelerating time-to-market for AI chips demands a parallel development approach, where silicon design, advanced packaging, and software stack development proceed concurrently. By co-optimizing hardware and software—including drivers, firmware, and AI frameworks—and integrating package and interconnect considerations simultaneously, teams can identify and resolve system-level challenges sooner. This parallelism ensures that when first silicon arrives, the package and software are ready for immediate bring-up and validation, significantly reducing overall development cycles.
• IP Integration and Reuse: Leveraging silicon-proven IP blocks for high-speed interfaces, memory controllers, security, and AI acceleration enables faster development, reduces risk, and ensures interoperability. Pre-verified IP also supports rapid adaptation to evolving interface standards and AI workloads.
• Advanced Packaging: Modern AI chips increasingly rely on advanced packaging techniques, such as 2.5D and 3D integration, to combine multiple chiplets or dies into a single package. These technologies provide higher bandwidth, lower latency, and greater flexibility in integrating best-in-class process technologies for compute, memory, and I/O. However, advanced packaging introduces new challenges in power delivery and thermal management. Accurate power and thermal simulation early in the design cycle is essential to prevent overheating, ensure reliable operation, and optimize overall system performance. By addressing these challenges up front, engineering teams can fully leverage advanced packaging to achieve the performance, energy efficiency, and scalability required for next-generation AI workloads.
• Early Software Development: Creating software-defined silicon for specific AI workloads requires initiating software development early in the AI chip design process to optimize performance and power and achieve rapid system bring-up once silicon arrives. By developing drivers and firmware and conducting workload testing in parallel with hardware using virtual prototypes, emulation, and prototyping platforms, software teams can begin integration and optimization well before first silicon, identify architectural bottlenecks, optimize performance and power, reduce overall project risk, and help ensure that the silicon is ready to run production-level workloads as soon as it arrives.
• Verification and Validation: Comprehensive verification is essential, including hardware-assisted verification (HAV) to accelerate both RTL verification and system-level validation to uncover corner-case bugs early. Running real-world AI workloads and software stacks on pre-silicon prototypes helps ensure functional correctness, performance, and interoperability.
• Security Solutions: As AI chips become central to critical infrastructure and applications, robust security solutions are essential throughout the silicon, software and system stack. AI workloads introduce unique security threats, such as training database poisoning—where adversarial data is injected to corrupt model behavior—and inference model tampering or theft, where unauthorized access or manipulation of deployed AI models can compromise system integrity or intellectual property. Addressing these risks requires a multi-layered security approach: integrating secure boot, hardware roots of trust, and cryptographic accelerators at the chip level; protecting model data and weights in memory and during computation; and supporting secure provisioning and update mechanisms. Advanced security IP, real-time monitoring, and lifecycle management tools help AI chip developers detect, prevent, and respond to emerging threats, ensuring trusted operation and protecting sensitive AI assets from development through deployment.
• Design for Manufacturing and Test (DFM/DFT): Manufacturability and testability must be considered from the outset. Incorporating robust DFM/DFT methodologies improves yield, reliability, and time-to-market, which is especially important for advanced process nodes and multi-die designs.
• Collaboration and Ecosystem: A successful AI chip program depends on close collaboration among architecture, design, verification, packaging, and software teams. Leveraging a robust ecosystem of EDA tools, IP vendors, foundries, packaging partners, and design services accelerates development and de-risks the path to production.

By embracing these considerations, engineering teams can maximize the probability of first-pass silicon success and deliver differentiated AI chips that meet the performance, energy efficiency, and reliability expectations of the market.

Synopsys is a leader in providing comprehensive solutions for AI chip development, empowering semiconductor design engineers, verification engineers, packaging engineers, software developers and test engineers, to bring cutting-edge AI chips to market faster and more efficiently.

Synopsys AI Chip Development Solutions:

• AI-Driven EDA: Synopsys integrates artificial intelligence and generative AI copilot assistance capabilities into electronic design automation (EDA) tools, helping design teams optimize power, performance, and area (PPA) while enhancing productivity and reducing time-to-market of AI chip designs.
• Silicon-Proven IP Solutions: Synopsys offers a broad portfolio of silicon-proven IP solutions, including high-speed interfaces (HBM, PCIe, Ethernet), memory controllers, and AI-optimized processing cores for leading edge process nodes supporting multiple foundries, reducing development risk and accelerating time-to-market.
• 2.5D and 3D Multi-Die Package Design: Advanced packaging design and simulation technologies from Synopsys enable the integration of high-bandwidth memory along with compute and I/O chiplets into multi-die packages, critical for supporting the performance and bandwidth needs of modern AI chips.
• Hardware-Assisted Verification (HAV): Hardware Assisted Verification (HAV) from Synopsys enables rapid, system-level validation of complex AI chips by running real workloads on emulation and prototyping hardware platforms, accelerating bug detection and helping ensure functional and performance correctness before silicon tape-out.
• Agentic AI and Engineering Automation: Synopsys is pioneering the use of AI agents to automate engineering workflows, further boosting productivity and innovation in AI chip design.

By leveraging Synopsys’ advanced EDA tools, IP solutions, package design and simulation technologies, HAV, and Agentic AI automation, engineering teams can efficiently address the challenges of designing, verifying, and manufacturing the next generation of AI chips.