Beyond Boundaries: Pushing Performance Limits with Chiplet Innovations#

Key Definitions#

• Chiplet: A modular block of silicon that performs a specific function.
• SoC (System-on-Chip): A large, monolithic die that integrates multiple system functionalities—like CPU cores, GPU, memory controllers—into a single semiconductor die.
• Advanced Packaging: Techniques used to integrate multiple chips or chiplets into a single package while minimizing interconnect overhead and improving performance.
• Interposer: A passive or active layer (often silicon-based) used to connect chiplets, providing high-density wiring.

Historical Perspective#

Historically, designers overcame the challenges of large monolithic designs through Multi-Chip Modules (MCMs) in the 1990s and early 2000s. MCMs integrated multiple dies in one package but had limited popularity due to cost constraints and interconnect complexities. Modern chiplets are a more refined approach, aided by advanced packaging technologies (e.g., 2.5D/3D integration) and better power/performance profiles.

Economic Advantages#

1. Yield Improvement: Large monolithic dies suffer from lower yield rates; as die size increases, the probability of a yield-lowering defect also increases. Chiplets counter this by distributing functionality across smaller dies, drastically improving overall yields.
2. Modular Reuse: Being able to reuse a chiplet design (for example, a GPU core or an AI accelerator block) reduces development costs and accelerates time-to-market.
3. Process Node Optimization: Different chiplets can be built on different process nodes. Critical, high-performance blocks may use the latest node (e.g., 5 nm), while less critical blocks can use older, cheaper nodes (e.g., 12 nm).

Flexibility and Scalability#

• Customization: Manufacturers can pick and choose which blocks to include. This modular approach opens up a wide range of product configurations without the need to redesign an entire SoC.
• Easier Upgrades: If a particular chiplet (say, for AI inference) needs an upgrade, designers can simply update that one module in the next product iteration while leaving the rest of the system unchanged.

Driving Innovation#

As the landscape changes, adopting chiplets helps companies remain agile. They can incorporate cutting-edge IP into specific chiplets while maintaining proven parts for other functionalities. This effectively lowers the barrier to incorporating specialized hardware (like AI or cryptography accelerators).

Monolithic SoC Approach#

Traditionally, an SoC design tries to place all functional blocks on a single die. This approach offers low-latency communication between modules but can lead to:

• Lower manufacturing yield for very large chips.
• More complex design cycles (longer verification times, packaging difficulties).
• Inflexibility when mixing IP across different process nodes.

Chiplet-Based Approach#

By contrast, the chiplet approach consists of multiple, smaller dies in a single package. Key advantages include:

• Smaller each-die footprint, leading to improved yields.
• Mix-and-match specialization at different process geometries.
• Simpler debugging and verification of smaller, modular dies.

However, challenges include ensuring that:

• Interconnect overhead does not become a performance bottleneck.
• Thermal management is optimized for a multi-die system.
• Packaging complexities are efficiently handled.

Use of Interposers#

To seamlessly stitch multiple dies together, chiplet-based systems sometimes use interposers or advanced packaging substrates:

2.5D Integration#

2.5D packaging is characterized by the use of an interposer to connect multiple active dies side by side. This interposer often contains high-density wiring layers, enabling thousands to millions of interconnects between chiplets, which significantly reduces latency and improves bandwidth compared to older techniques.

• Advantages: High bandwidth, dense interconnect, relatively mature.
• Limitations: More expensive than conventional PCB-level integration.

3D Stacking#

3D stacking is an advanced approach involving stacking dies vertically. Through-Silicon Vias (TSVs) connect layers, reducing footprint and improving power efficiency by minimizing wire length. This approach allows extremely high bandwidth—especially important for memory-on-logic stacks.

• Advantages: Minimal footprint, highest bandwidth, improved power efficiency.
• Limitations: Higher complexity in manufacturing, thermal management challenges.

Advanced Interconnect Protocols#

Communication between chiplets requires carefully designed interconnect protocols. These can be proprietary or standardized. Examples include:

• CCIX (Cache Coherent Interconnect for Accelerators)
• CXL (Compute Express Link)
• OpenCAPI

These protocols aim to reduce overhead between chiplets and ensure cache coherency, particularly important for high-performance computing architectures.

Hierarchical System-Level Design#

When planning a chiplet-based system, consider both top-down and bottom-up perspectives:

1. Top-Down: Start with system-level requirements (power, area, performance targets) and allocate functionality to each chiplet.
2. Bottom-Up: Evaluate design feasibility for each chiplet independently, ensuring each block can meet performance specs while fitting within the selected process node.

Thermal Management#

Multi-die operation can lead to uneven heat dissipation. Hot spots may appear where the densest or most active chiplets reside. Proper thermal planning includes:

• Heat spreaders to distribute heat more evenly.
• Separate power domains per chiplet for power gating inactive blocks.
• Thermal sensors and real-time control loops.

Power Delivery Network (PDN)#

Splitting a system into multiple dies complicates power delivery. Each chiplet may have unique voltage rails. Minimizing IR drop and ensuring stable power across interconnections is a primary concern, often handled by advanced package-level PDN designs.

Validation and Testing#

Tests must be performed both on individual chiplets and on the integrated package. Known-good-die (KGD) processes are vital to verify that each chiplet meets specification before final packaging, reducing overall product defects and waste.

AMD Ryzen#

AMD made headlines by using a multi-chiplet design for its Ryzen and EPYC processors. A smaller I/O die is paired with multiple CPU chiplet dies, enabling high core counts and improved yields:

• Zen Microarchitecture: CPU cores are segmented into chiplets (often referred to as Core Complex Dies, CCDs).
• I/O Die: Memory controllers, PCIe interfaces, and other connectivity functions reside on a separate die.

This design provides a flexible approach to scale core counts and integrate different features without redesigning a single large monolithic die.

Intel’s Foveros and EMIB#

Intel leverages advanced packaging through Foveros (3D stacking) and EMIB (Embedded Multi-die Interconnect Bridge) technologies. This allows stacking logic on top of each other (Foveros) or placing multiple dies side by side with a small silicon bridge (EMIB).

• Foveros: Known for 3D stacking—used in Lakefield processors.
• EMIB: Reduces the need for a full interposer by acting as a localized high-density interconnect region.

Nvidia and Multi-GPU Modules#

For GPU scaling, Nvidia has explored MCM (Multi-Chip Module) and chiplet-like designs, especially for data center accelerators. Partitioning GPU cores or HPC blocks into multiple dies can mitigate yield issues on large GPU designs.

Example 1: Register-Transfer Level (RTL) Stub#

In hardware description languages (HDL) like Verilog or VHDL, you might define separate modules representing chiplets:

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module cpu_chiplet (
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    input  clk,
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    input  reset,
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    input  [31:0] data_in,
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    output [31:0] data_out
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    // Additional inputs and outputs...
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);
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// CPU logic here, e.g., pipeline stages
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endmodule
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module io_chiplet (
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    input  clk,
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    input  reset,
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    // ...
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);
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// I/O logic here, e.g., memory controllers
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endmodule

Then, in a higher-level system module, you instantiate and connect them:

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module top_integration (
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    input  global_clk,
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    input  global_reset,
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    // ...
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);
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wire [31:0] bus_data_cpu;
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wire [31:0] bus_data_io;
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// Instantiate CPU chiplet
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cpu_chiplet u_cpu (
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    .clk(global_clk),
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    .reset(global_reset),
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    .data_in(bus_data_io),
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    .data_out(bus_data_cpu)
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);
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// Instantiate I/O chiplet
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io_chiplet u_io (
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    .clk(global_clk),
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    .reset(global_reset)
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    // ...
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);
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// Additional interconnect logic or direct bus linking
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// bus_data_io <= bus_data_cpu or a dedicated arbitration block
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endmodule

Example 2: High-Level Chiplet Configuration#

High-level script (pseudo-code) that might run prior to hardware synthesis, describing how many chiplets and their arrangement:

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# Imaginary Python script for chiplet configuration
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class ChipletConfig:
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    def __init__(self, name, process_node, function_blocks):
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        self.name = name
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        self.process_node = process_node
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        self.function_blocks = function_blocks
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cpu_chiplet_config = ChipletConfig(
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    name="CPU_Chiplet",
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    process_node="5nm",
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    function_blocks=["ALU_Cluster", "L1_Cache", "Scheduler"]
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)
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io_chiplet_config = ChipletConfig(
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    name="IO_Chiplet",
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    process_node="12nm",
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    function_blocks=["PCIe_Controller", "Memory_Controller"]
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)
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# Outline for system-level aggregator
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system_chiplets = [cpu_chiplet_config, io_chiplet_config]
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for chiplet in system_chiplets:
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    print(f"Designing {chiplet.name} on {chiplet.process_node} node")
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    for block in chiplet.function_blocks:
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        print(f"  - Integrating {block}")

In a real project, such a script might feed data to an automated toolchain that matches blocks to the right process node, sets up floor planning, and triggers the packaging design phase.

Key Metrics#

1. Latency: The time it takes to move data between chiplets. Minimizing the distance (i.e., employing advanced packaging) helps reduce overall latency.
2. Bandwidth: The number of bits per second transferred between chiplets—a crucial factor for memory-intensive applications.
3. Power Efficiency: Each interface consumes power, so power gating and advanced interconnect design become essential in multi-die systems.

Test and Measurement#

• Protocol analyzers: Check the correctness and timing of inter-chiplet protocols.
• Thermal imaging: Visualize hotspots across the package surface.
• Automated test equipment (ATE): Evaluate each chiplet’s performance to ensure compliance with specifications (KGD methodology).

Example Test Flow#

1. Individual Chiplet Validation: Power on each chiplet in isolation, verify internal functionality using a test harness.
2. Integration Testing: Attach the chiplets onto an interposer or advanced substrate, run key system-level tests (boot service routines, memory read/write, partial load tests).
3. Full System Launch: Evaluate real workloads (e.g., HPC, AI inference, gaming) to confirm performance targets are met.

1. Heterogeneous Integration#

As demand grows for specialized accelerators, heterogeneous integration is key. This brings together CPU chiplets, GPU chiplets, AI/ML accelerators, or even specialized networking blocks into one package. A single system can thus handle diverse workloads efficiently.

Challenges:

• Varying thermal requirements (GPU vs. CPU)
• Complex data paths for specialized workloads
• Managing concurrency and resource scheduling in a multi-accelerator environment

2. Security Implications#

With multiple chiplets possibly sourced from different vendors, security becomes a top priority:

• Root of Trust: Ensuring each chiplet can authenticate itself.
• Data Encryption: Protecting in-package data transfers if untrusted or partially trusted chiplets exist.
• Physical Security: Hardening chiplets against side-channel attacks, especially with 2.5D or 3D integration.

3. Multi-Chip Synchronization#

High-performance computing often requires precise synchronization. Ensuring consistent clock distribution across multiple dies demands advanced clock tree planning, potentially with on-chip phase-locked loops (PLLs), or even distributed network-on-chip (NoC) concepts extended to package-level.

4. Software and Firmware Implications#

From an OS perspective, multi-chip systems can appear as a single SoC or as discrete devices. Firmware must orchestrate power-up sequencing, health checks, and communication initialization. Operating systems may need updated drivers, memory controllers, or scheduling algorithms that treat each chiplet as part of a unified system.

5. Machine Learning at the Edge#

Many edge devices can benefit from modular designs. For instance, an AI accelerator chiplet can be integrated with a CPU chiplet in a single package for quick product customization. Moreover, advanced packaging can integrate specialized IP (e.g., sensor-fusion, encryption) at scale.

6. Future of Chiplets: Universal Interconnect Standards#

One of the biggest developments in this space is the push towards universal standards for chiplet connectivity. Initiatives such as the “Universal Chiplet Interconnect Express” (UCIe) aim to create an open specification that standardizes how chiplets communicate. If such a standard gains widespread adoption, it could reduce complexity and cost, spurring a new wave of innovation as IP vendors race to provide drop-in chiplets for varied functionalities.