Revolutionizing Multi-Die Design: AI, Cloud, and Thermal Solutions for Next-Gen Chips

The article is about advancements in multi-die design, focusing on solutions provided by Ansys and Synopsys for addressing the increasing complexity of semiconductor development. The discussion revolves around methodologies that improve power, performance, and area (PPA) outcomes for multi-die systems.

Key points include:

  1. Thermal and Power Challenges: Multi-die systems pose challenges in managing heat and power. Advanced simulation tools from Ansys help in identifying and resolving thermal issues early in the design process, ensuring better reliability.

  2. Design Space Optimization: Synopsys utilizes AI-based tools like DSO.ai, which leverage reinforcement learning to explore vast design spaces efficiently. This allows for improved PPA by optimizing individual blocks before integration into larger systems. This technology enables significant productivity gains and power reductions across applications like GPUs, data centers, and mobile devices​

  3. Fast Emulation and Verification: Tools like Synopsys ZeBu Empower accelerate pre-silicon power verification, enabling designers to test realistic workloads over billions of cycles. This approach reduces risks associated with power bugs and enhances early identification of optimization opportunities​

  4. Cloud-Based Processing: To handle the computational demands of such advanced design workflows, Synopsys and Ansys promote cloud-based solutions. This ensures scalability and parallelism in simulations and analyses, significantly cutting down processing time while maintaining accuracy.

These innovations demonstrate how advanced EDA tools are transforming multi-die design workflows by addressing the trade-offs between power efficiency, performance, and cost-effectiveness. 


Multi-die design technologies from Ansys and Synopsys target several critical application areas where performance, power efficiency, and scalability are paramount:

Data Centers:

High-performance SoCs for hyperscale data centers require chips that are energy-efficient and capable of handling large workloads. These designs benefit from thermal management and power optimization to maintain performance while reducing energy costs​

5G Communication:

Chips used in 5G infrastructure, including radio head chips, demand optimization for parallel processing and low-latency performance. Accurate power profiling ensures these chips meet performance goals within strict power budgets​

Artificial Intelligence (AI):

AI processors for applications like machine learning require precise power management. Tools help in profiling power across AI workloads to balance performance and power efficiency, crucial for edge and server-based AI applications​

Mobile Devices:

Smartphones and other mobile devices prioritize compact chip designs with low power consumption for longer battery life. Multi-die solutions are instrumental in meeting these design goals while managing complex workloads efficiently​

Automotive Systems:

Advanced Driver-Assistance Systems (ADAS) and autonomous driving rely on multi-die SoCs for real-time processing of sensor data. Efficient thermal and power management is vital to ensure reliability under varying conditions.

High-Performance Computing (HPC):

HPC systems, including GPUs used for simulations, gaming, and scientific research, require optimized PPA to handle intensive workloads while minimizing heat and power demands​

Consumer Electronics:

Multi-die solutions support the integration of various functionalities, such as connectivity and compute power, into compact designs used in IoT devices and smart gadgets.

These applications highlight the transformative potential of multi-die designs in addressing industry demands for energy efficiency, performance scaling, and design integration.


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