How Chiplet Packaging Builds an AI Brain

“The AI brain isn’t one big chip—it’s a team of tiny ones!” Chiplet packaging is reshaping the future of AI hardware. Learn how tech giants like AMD, Intel, and NVIDIA are assembling the next-gen processors like LEGO. Read this explainer and get future-ready.

In the era of rapidly advancing Artificial Intelligence (AI), from smart speakers to autonomous vehicles, and from medical diagnostics to large language models, everything depends on a powerful computing core—the AI brain. This “brain” is no longer a massive, monolithic chip. Instead, it is an intricate system constructed from multiple miniature chips known as chiplets. Much like assembling LEGO bricks, chiplet packaging allows hardware to be modular and scalable, delivering both performance and flexibility.

This chiplet-driven semiconductor revolution is not only a technical innovation but also a fundamental shift in the supply chain, manufacturing strategies, and business models. From design and packaging to system architecture, chiplet technology is quietly rewriting the rules of the chip world. In this article, we’ll break down what a chiplet is, how they are assembled into an AI processor, and what impact they have on the industry.

Global Foundries and Chiplet Industry Landscape

As chiplet architectures gain traction, global semiconductor foundries are ramping up their investments in relevant technologies and standardizations. TSMC (Taiwan Semiconductor Manufacturing Company) leads the way with advanced packaging technologies such as CoWoS and InFO, and is pushing forward with its System on Integrated Chips (SoIC) and 3D Fabric to enable heterogeneous integration. Intel, on the other hand, has developed Foveros and EMIB, while collaborating on the Universal Chiplet Interconnect Express (UCIe) standard to enable cross-vendor compatibility.

Samsung has launched its X-Cube platform, emphasizing vertical integration for custom AI chips. Meanwhile, UMC and ASE are innovating in 2.5D, fan-out, and back-side power delivery (BSP) technologies, providing versatile packaging solutions for High-Performance Computing (HPC) and AI systems.

Packaging is no longer a back-end process. It is increasingly integrated into the early design stage, evolving into strategies such as System-in-Package (SiP) and Package-as-System (PaS). Foundries are transitioning from being chip manufacturers to system integrators involved in simulation, thermal design, and final testing.

Countries like China, South Korea, and Japan are also investing heavily in chiplet R&D as part of national semiconductor strategies. For example, Tsinghua University and Cambricon in China are developing localized chiplet standards, while South Korea’s government collaborates with SK hynix to create a domestic chiplet + HBM ecosystem. This indicates the competition has escalated from commercial race to technology sovereignty.

Chiplet Assembly Methods (MCM, 2.5D, 3D)

To orchestrate different functional chiplets into a coherent AI system, advanced packaging technologies are key. Here are the main types:

• Multi-Chip Module (MCM): The most basic form, where multiple bare dies are mounted on the same substrate and connected via wire bonding or solder bumps. While cost-effective, it suffers from long signal paths and low integration density—less ideal for high-speed AI applications.

• 2.5D Packaging (with Interposer): This uses a silicon interposer between the substrate and chiplets, enabling short, high-density connections without vertical stacking. Widely used in HPC and AI, with notable examples like AMD’s MI300 and Intel’s Ponte Vecchio.

• 3D Stacking: Chiplets are vertically stacked and connected using Through-Silicon Vias (TSVs). This shortens data paths and boosts bandwidth, but requires solutions to heat dissipation and mechanical stress. TSMC’s SoIC and Intel’s Foveros are leading examples.

Heterogeneous Integration and Modular AI Architecture

In AI processors, chiplet design is more than just assembly—it’s a system-level strategy enabling:

• Heterogeneous Integration: AI workloads demand various computing engines like CPUs, GPUs, and NPUs. Chiplet packaging allows them to be fabricated separately and then integrated into a single system, reducing latency and energy consumption.

• Process Optimization: Different modules require different process nodes. For instance, memory I/O controllers may use mature 28nm technology, while compute cores leverage cutting-edge 3nm. Chiplets enable co-existence of diverse nodes for cost-performance balance.

• Modular and Agile Design: Need to upgrade your AI engine? Swap out just the compute chiplet. This shortens time-to-market and allows for rapid iteration.

Additionally, modular chiplet architecture facilitates global supply chain collaboration. Each module can be developed by specialized vendors, fostering innovation and allowing smaller players to enter the market.

Real-World Use Cases (AMD Ryzen, NVIDIA Grace Hopper)

The chiplet approach is already present in leading products:

• AMD Ryzen and EPYC: AMD pioneered chiplet deployment at scale. Ryzen desktop CPUs separate I/O chiplets from compute dies, balancing cost and performance. EPYC server chips use multiple Core Complex Dies (CCDs) with an I/O Die (IOD), scaling to dozens of cores efficiently.

• NVIDIA Grace Hopper Superchip: Released in 2023, this combines a Grace CPU and Hopper GPU using chip-to-chip interconnects and HBM memory. NVLink-C2C enables over 900GB/s internal bandwidth, optimized for AI training and inference workloads.

These examples show chiplets are not future theory—they are powering the next wave of high-performance computing.

Investment Momentum and Industry Expansion

Yole Group forecasts the chiplet market will grow from $4.8 billion in 2024 to $136 billion by 2027, and over $205 billion by 2032. This rapid growth is driven by adoption in AI, cloud, and edge computing.

Startups like Marvell, Tenstorrent, and Esperanto Technologies are joining giants like Intel, AMD, and NVIDIA in shaping the ecosystem. Investors are watching the entire value chain:

• Upstream: EDA tools (Cadence, Synopsys) supporting chiplet simulation and verification

• Midstream: Advanced packaging and OSAT (ASE, Amkor, SPIL)

• Downstream: AI platforms, data centers, and automotive applications

Geopolitics also plays a role. The U.S. CHIPS and Science Act and the EU Chips Act are pushing for domestic chiplet manufacturing and IP sovereignty, making chiplets a core component of national strategy.

Green Chips and ESG Strategy

AI models are power-hungry. Chiplets help lower the carbon footprint by:

• Using mature nodes for non-critical functions

• Enabling reusability of chiplet modules

• Improving yield by fabricating smaller dies

TSMC and NVIDIA are disclosing packaging-related carbon data, while startups like Graphcore are exploring co-designed chip-dissipation layouts. ESG metrics like modular reuse and recyclability may become standard disclosures in the future.

Future Outlook: Quantum Integration and Open Ecosystems

Looking ahead, chiplets may extend into quantum and neuromorphic computing. Imagine placing a quantum controller chiplet next to a classical logic unit—it’s already in early trials by IBM and PsiQuantum.

Open standards like UCIe are also game-changers, enabling multi-vendor chiplets to communicate seamlessly. This creates an “App Store for Chips” where developers mix and match modules like never before.

Cadence, Synopsys, and others are expanding EDA toolchains to support chiplet-based architectures, lowering barriers and accelerating innovation across the board.