Piecing It Together: Why Chiplets Are the Future of CPU Scalability
Computer processors are at the heart of modern technology, powering everything from smartphones to supercomputers. Over the decades, CPU design has evolved dramatically to meet growing demands for performance, efficiency, and complex functionality. A major shift is now underway: rather than increasing core counts in a single monolithic piece of silicon, the industry is embracing chiplets—smaller, modular silicon dies assembled into a larger package. This blog post explores why chiplets are increasingly seen as the future of CPU scalability and how they address real-world challenges in manufacturing, performance, and cost.
Table of Contents
- Introducing Chiplets: A Brief Overview
- How Monolithic CPUs are Manufactured
- Limitations of Monolithic Designs
- What Exactly Are Chiplets?
- Benefits of the Chiplet Approach
- Technical Considerations for Chiplet-Based CPUs
- Packaging Technologies for Chiplets
- Examples and Use Cases
- A Simple Parallel Computing Example
- How Chiplets Influence the Software Stack
- Challenges and Limitations
- Future Directions and Research
- Summary and Final Thoughts
Introducing Chiplets: A Brief Overview
As computing needs grow, so does the complexity of the chips needed to power modern data centers, gaming rigs, and everyday devices. For decades, the path to higher performance was fairly straightforward: pack more transistors and cores on a single silicon wafer, shrink the process technology (e.g., from 14nm down to 5nm), and increase clock speeds.
However, physical and economic realities have made this “more of the same” approach challenging:
- Each new “shrink” in process node introduces significant complexities and costs.
- Larger chips have higher defect rates, resulting in lower yields and more wasted wafer area.
- The demand for specialized functionality (e.g., AI accelerators, graphics, and high-speed I/O) requires greater design flexibility.
Chiplets address these problems by taking a modular approach. Instead of designing one big piece of silicon, you break it into smaller “chiplets,” each optimized for a specific function. These chiplets are then combined in a package to form a unified processor.
How Monolithic CPUs are Manufactured
To understand how chiplets differ from traditional SOC (system on chip) or CPU design, it helps to look at how a monolithic CPU is typically manufactured.
- Design Phase: Engineers determine the architecture—how many cores, the cache structure, I/O interfaces, and so on.
- Mask Creation: Extremely expensive photomasks (reticles) are made, allowing the features to be “printed” onto the silicon wafers.
- Lithography: Light passes through the masks onto the wafer coated with photoresist, transferring the circuit patterns.
- Etching and Deposition: Chemical processes remove layers of material (like silicon or metal) and deposit new layers (like copper interconnects).
- Testing and Packaging: After the wafer is complete, it’s cut into individual dies. Each die is tested; the functional ones are packaged and sold.
Wafer Defects and Yields
One of the biggest manufacturing challenges is the presence of random defects on the wafer. The larger the chip:
- The higher likelihood that a single defect can render the entire die unusable.
- The lower the overall yield (percentage of working dies).
- The higher the cost per die, since you’re effectively discarding entire large pieces of silicon when they fail.
As chips grow larger and more complex, yields can drop significantly, which impacts the final product’s cost. Manufacturers keep trying to increase yields, but physical limitations and randomness come into play, making large monolithic dies increasingly expensive to produce.
Limitations of Monolithic Designs
Complexity and Costs
Engineering a single die that does everything—multiple cores, large caches, special accelerators, I/O, memory controllers—becomes an enormous task. Each of these components must be validated at every new process node. This increases design complexity, time to market, and cost.
Scalability
More cores on a single piece of silicon means bigger chip area. This leads to:
- Increased likelihood of defects.
- Longer interconnects, which introduce latency and consume more power.
- Design challenges related to routing power and signals across a large chip.
Thermal Constraints
A single large die may face uneven heat distribution. Certain areas (like CPU cores under heavy load) become hotspots, while others remain cooler. This imbalance can limit clock speeds or require complicated cooling solutions.
What Exactly Are Chiplets?
A chiplet is a smaller, self-contained piece of silicon that performs a specific function. Instead of integrating everything into one large die, a product might use multiple chiplets:
- One chiplet for CPU cores
- Another for GPU or AI acceleration
- Another for memory and I/O
- Additional chiplets for specialized tasks (data encryption, specialized ML tasks, etc.)
All these chiplets are placed on an interposer or substrate and interconnected to function as a cohesive system. This modular design offers a new way to increase functionality and performance without building ever-larger monolithic chips.
Common Terminology
- Interposer: A layer (often silicon-based) that provides wiring between chiplets in an advanced package.
- Substrate: The base material that supports the chiplets and includes electrical connections.
- Die-to-Die (D2D) Interconnect: Specialized physical interfaces that enable chiplets to communicate with each other.
Benefits of the Chiplet Approach
1. Improved Yield
Because each chiplet is smaller, the probability of defects rendering the entire product unusable decreases. If one chiplet is defective, you can often replace just that chiplet rather than discarding an otherwise functional large die. This increases yield and reduces waste.
2. Reduced Manufacturing Cost
By breaking down the CPU into smaller parts, each part can use an optimized process node or even be outsourced to different foundries if necessary. For example:
- CPU cores might benefit from a leading-edge 5nm node.
- I/O or analog components might be fabricated on cheaper, more mature 12nm or 14nm nodes.
This approach saves money because you only use the most expensive processes where they are most beneficial.
3. Flexibility and Customization
Chiplets can be mixed and matched. A manufacturer can take the same CPU core chiplet and pair it with different GPU or AI accelerator chiplets based on customer needs. This reusability speeds development and product diversification.
4. Better Scaling to Higher Core Counts
When you add more cores in a chiplet-based approach, you simply add more CPU chiplets. This approach simplifies design and testing, making higher core counts more feasible while improving reliability.
5. Lower Design Risk
Each chiplet is already validated. Any changes to a particular part of the design can be isolated to that chiplet without affecting the rest. This speeds up iterative improvements.
Technical Considerations for Chiplet-Based CPUs
Interconnect Technologies
When multiple chiplets are assembled, they need a way to communicate. Interconnect strategies vary:
- Silicon Interposers: Requires sophisticated manufacturing but provides high-density, low-latency connections.
- Organic Substrates: Easier to produce but may have higher latency and lower interconnect density.
- Advanced Packaging: Techniques like 2.5D or 3D packaging can stack chiplets vertically or place them side-by-side on an interposer.
Latency and Bandwidth
Ensuring that data can flow effectively across chiplets is crucial to overall performance. Engineers must carefully design protocols and physical layers to minimize latency penalties while maximizing bandwidth.
Power Delivery
In a monolithic design, power is distributed across the silicon. With chiplets, power delivery must be partitioned. Each chiplet needs its own power planes and regulators, which can add complexity but also allow more nuanced power management.
Thermal Management
Since chiplets are physically smaller and distributed, heat can be more localized—or more spread out, depending on the layout. Cooling solutions must consider these hot spots and inter-chip thermal interference.
Packaging Technologies for Chiplets
Packaging plays a central role in making chiplets work seamlessly. Several packaging solutions are used in modern chiplet-based CPUs:
- 2.5D Packaging: Chiplets are placed side by side on a silicon interposer that provides high-density interconnects.
- 3D Stacking: One chiplet is stacked on top of another. TSVs (Through-Silicon Vias) provide vertical connections between layers.
- Hybrid Bonding: Direct copper-to-copper bonding between chiplets, enabling high bandwidth and low latency.
- Embedded Multi-Die Interconnect Bridge (EMIB): Intel’s technology that places a small silicon bridge within an organic substrate to connect chiplets with high-speed links.
Each approach has trade-offs in cost, performance, and manufacturability. As process technologies advance, we’ll likely see more refined and specialized packaging solutions emerge.
Examples and Use Cases
A variety of companies are adopting chiplet approaches:
- AMD: Introduced Zen-based Ryzen and EPYC processors using multi-die “chiplet” designs. They use a central I/O die on a larger process node and compute chiplets on a cutting-edge node.
- Intel: Uses EMIB and Foveros (3D stacking) for bridging different chiplets, integrating CPU, GPU, and memory.
- Apple: M1 Ultra fuses two M1 Max dies using a high-speed interconnect, though it’s more of a multi-die approach than a fully modular chiplet architecture.
- NVIDIA: For GPUs, future designs are expected to use chiplets to break massive GPU dies into manageable pieces.
A Quick Comparison: Monolithic vs. Chiplet
Below is a brief table comparing the two approaches:
| Aspect | Monolithic CPU | Chiplet CPU |
|---|---|---|
| Defect Sensitivity | High | Lower |
| Manufacturing Cost | Potentially very high | More optimized per function |
| Scalability | Difficult beyond a point | Easier to add more chiplets |
| Power Management | Uniform across the die | Each chiplet can be isolated |
| Performance | Generally high but can be limited by large interconnects | Potentially higher with well-designed interconnects |
A Simple Parallel Computing Example
While the hardware architecture is crucial, software also plays a role in how effectively we utilize multi-core or multi-chiplet designs. Below is a simple C/C++ code snippet demonstrating parallel work distribution. Suppose we wanted to sum a large array in parallel, taking advantage of multiple cores (or multiple chiplets, each providing a set of cores).
#include <iostream>#include <vector>#include <thread>
void partialSum(const std::vector<int>& data, long long& result, size_t start, size_t end) { long long tempSum = 0; for (size_t i = start; i < end; ++i) { tempSum += data[i]; } result = tempSum;}
int main() { const size_t dataSize = 100000000; std::vector<int> data(dataSize, 1); // fill with 1s
long long result1 = 0, result2 = 0; size_t mid = dataSize / 2;
// Launch two threads std::thread t1(partialSum, std::cref(data), std::ref(result1), 0, mid); std::thread t2(partialSum, std::cref(data), std::ref(result2), mid, dataSize);
t1.join(); t2.join();
long long total = result1 + result2; std::cout << "Total sum: " << total << std::endl;
return 0;}Assume you have a chiplet-based CPU with multiple CPU chiplets. Each chiplet might manage a set of threads. The key is that the operating system schedules these threads across the core complexes, leveraging the hardware. While this is a simplistic example, it highlights how multi-core (and by extension, multi-chiplet) systems can handle workloads in parallel.
How Chiplets Influence the Software Stack
Operating System Scheduling
Operating systems like Windows, Linux, and macOS must become “chiplet-aware.” For example, some of AMD’s architectures have multiple Core Complex Dies (CCDs) sharing an I/O die. The OS needs to know how to distribute threads to take advantage of local caches and memory controllers. The same principle applies to chiplet-based designs from other vendors.
Memory Management
In a multi-chiplet design, each chiplet could have different memory access latencies if memory controllers are distributed. NUMA (Non-Uniform Memory Access) becomes more relevant to optimize performance.
Compiler and Toolchain Optimizations
Optimizing for a chiplet-based CPU can involve advanced compiler techniques for partitioning workloads. In HPC (High-Performance Computing), frameworks like MPI (Message Passing Interface) and OpenMP can be tweaked to recognize the topology of chiplets and schedule tasks accordingly.
Firmware and BIOS
At an even lower level, management of power states, clock gating, and initialization sequences may be unique to each chiplet. Firmware developers must ensure seamless orchestration so the system can boot up and run applications without issues.
Challenges and Limitations
Despite the considerable advantages, chiplet-based designs do come with complexities:
-
Interconnect Overheads
High-speed, low-latency interconnects are needed. If interconnect quality is subpar, performance gains can be negated. -
Thermal Hotspots
While distributing components can help with heat dissipation, it can also create localized hotspots where multiple high-power chiplets are clustered. -
Complex Packaging
Advanced packaging technologies are not cheap and require strong industry partnerships (e.g., with TSMC or Intel’s packaging solutions). -
Design and Validation Complexity
Each chiplet requires its own design verification, and the package as a whole must be tested for system-level interactions. -
Software Adaptation
Software—especially operating systems and hypervisors—must be acutely aware of the chiplet topology to optimize scheduling and resource allocation. -
Supply Chain Management
Different chiplets may be manufactured by different foundries or in different process nodes. Coordinating supply chains and ensuring consistent quality can be challenging.
Future Directions and Research
The concept of chiplets opens up vast possibilities for integrating heterogeneous functions in one package. Some future research directions include:
-
3D Stacking for More Layers
Rather than just placing chiplets side by side, advanced designs can stack multiple layers (e.g., CPU on top, memory below) with TSVs or hybrid bonding. This can drastically increase component density and reduce interconnect lengths. -
Universal Interconnect Standards
Initiatives like the UCIe (Universal Chiplet Interconnect Express) standard are emerging to unify the way chiplets communicate. This could enable truly “plug-and-play” chiplets from various vendors. -
Specialized Accelerators
As AI grows, we might see CPU chiplets paired with specialized accelerators for machine learning, cryptography, or even quantum co-processors. -
Reconfigurable Architecture
Future chiplets might be more programmable, allowing real-time reconfiguration of connections or functionality based on workload demands. -
Security Considerations
Multiple chiplets mean more interfaces and thus more potential attack surfaces. Security at the hardware level will be an important research area.
Professional-Level Expansions: Disaggregated Systems
For data centers and HPC environments, chiplets can enable truly disaggregated computing resources:
- Disaggregated Memory: Traditional server designs couple memory closely with CPUs. Chiplets could allow high-bandwidth memory (HBM) on one chiplet, while CPU cores exist on another, each optimized in different nodes.
- Networking/Interconnect Chiplets: Instead of a single NIC, a multi-chiplet approach might embed advanced networking hardware directly adjacent to CPU cores for ultra-low latency.
- Scalable AI Training: Instead of large monolithic AI accelerators, multiple AI chiplets can be tiled together to scale out processing power for training massive models.
- Integrated Photonics: Researchers are working on “silicon photonics” chiplets that use optical signals to communicate, reducing latency and power consumption for HPC.
These ideas point to a future where entire data centers are composed of building blocks that can be rearranged to meet specific workloads, drastically improving both efficiency and performance.
Summary and Final Thoughts
Chiplets represent a fundamental shift in how we design and build CPUs. By embracing modularity, they address many of the pain points associated with large, monolithic designs:
- Higher yield and lower cost through smaller dies and better binning.
- The ability to mix process nodes for optimal performance-per-dollar.
- Easier scalability to high core counts and specialized functionality.
- A path to more advanced 3D stacking solutions.
However, the move to chiplets also introduces new challenges in packaging, interconnect design, thermal management, and system-level software adaptation. As manufacturing processes continue to shrink and demands for specialized performance grow, chiplet-based architectures stand out as a powerful solution to keep CPUs scaling.
In the coming years, we can expect to see more innovation in chiplet interconnect technologies, standardization initiatives, and advanced packaging techniques. For both hardware and software professionals, understanding the benefits and challenges of chiplets will be key to leveraging the next wave of CPU innovation. Whether you’re developing enterprise servers, consumer devices, or AI supercomputers, chiplets can offer a flexible, powerful, and cost-effective route to building the high-performance systems of tomorrow.