The Growing Complexity of Semiconductor and Chip Design
Semiconductor companies are facing the pressure of increasing complexity, smaller processor nodes, and higher performance expectations as they design and manufacture electronic systems, including printed circuit boards (PCBs) and integrated circuits (ICs). By providing high processing performance and support for AI automation, HPC delivers essential infrastructure for Electronic Design Automation (EDA), enabling faster chip design cycles, more simulations, improved product performance, and faster time to market.
Why EDA Workloads Require High Performance Computing
Traditional IT environments can't support the scale and intensity of EDA workloads because complex simulation and verification processes need to be divided into a sequence of tasks that must be run one after the other on the same processor. HPC uses parallel processing to carry out multiple workloads simultaneously. With HPC, semiconductor manufacturers gain the parallel processing, high-throughput storage, and low-latency infrastructure they need to run automated chip design, verification, timing analysis, and simulation processes for EDA to ensure chips meet speed requirements.
From Design Iteration to Faster Time-to-Market
The ability to use EDA to iterate quickly is critical to successful semiconductor development. Engineering teams must continuously improve the performance of chips to keep up with increasing computational demands. HPC environments provide processing acceleration through GPUs, allowing chip design teams to run more simulations in less time, accelerating development cycles and reducing time to market for new chips.
How IBM HPC Platforms Support EDA Environments
IBM plays an important role in allowing semiconductor companies to create large-scale computing environments that support engineering, analytics, and AI-driven programs for chip design using EDA. IBM HPC offerings provide the scalable infrastructure needed for advanced simulation, high-volume data processing, and performance-intensive workflows, including validation and timing analysis.
IBM HPC uses parallel computing to run multiple tasks simultaneously on numerous servers using thousands of processor cores. IBM HPC clusters are made up of multiple high-speed servers that are networked with a centralized scheduler that manages parallel computing workloads, enabling them to carry out the complex mathematical calculations, machine learning models, and automated tasks performed during EDA-aided chip engineering.
IBM Technologies Powering EDA HPC Workloads
IBM's technology ecosystem supports EDA workflows for semiconductor chip design. IBM HPC integrates with other IBM solutions to enhance computing, storage, and AI capabilities so semiconductor companies can process large design, simulation, and validation datasets efficiently.
IBM FlashSystem storage enables semiconductor companies to implement all-flash arrays as high-performance storage frameworks for the data that fuels EDA. The latest FlashSystem storage is AI-driven, dynamic storage that provides the high level of storage capacity needed to run EDA workflows.
IBM Storage Scale and advanced encryption enable the secure management of huge volumes of data related to simulation, verification, and timing analysis.
IBM HPC integrates with IBM watsonx, enabling semiconductor companies to harness its core AI capabilities for EDA. Watsonx.data allows chip manufacturers to bring together all their engineering, testing, and performance data from different sources to ensure the information being used to carry out AI-powered EDA operations is complete and accurate. Watsonx.governance helps semiconductor companies scale AI use responsibly by unifying the directing, management, and monitoring of AI operations. Watsonx Orchestrate supports the use of agentic AI for automating EDA processes. Orchestrate allows multiple AI agents to collaborate, automating complex workflows related to chip design and verification.
Key Use Cases for HPC in Electronic Design Automation
HPC has many EDA use cases that create measurable value across the chip production lifecycle. HPC generates Return on Investment (ROI) for semiconductor companies by reducing the compute costs of EDA while improving throughput and accelerating time-to-market for new chips.
Chip Simulation
HPC provides the compute power needed for EDA to conduct the predictive analytics needed during chip simulations. EDA simulation tools must use a description of a circuit to forecast how it will behave under real-world conditions.
Design Verification
HPC supports EDA design verification tools that examine the logical representation of a chip that is under development to assess if the design is connected properly and will deliver the required performance. The processing power of HPC is needed to analyze the chip based on complex requirements that can include thousands of rules.
Timing Analysis
HPC in EDA accelerates timing analysis by leveraging massively parallel, high-memory, multi-core, or cloud-based systems. HPC can handle complex, large-scale chip designs by distributing workload across thousands of cores, reducing turnaround time for verifying that chips meet speed requirements.
Power Analysis
HPC supports the computing needs of EDA power analysis tools which calculate power consumption and evaluate the integrity of ICs and PCBs to optimize performance. With HPC resources, EDA tools can run simulations of switching activity to check for bottlenecks, supporting the creation of power-efficient designs that extend the battery life of electronics.
Large Circuit Models
HPC supports the use of Large Circuit Models (LCMs), an AI-powered methodology for understanding and conveying the significance of circuit data to develop more efficient and innovative chip design methodologies.
Supporting AI and Advanced Analytics in Chip Design
AI and machine learning are essential for chip design. Chips are far too complex to be designed manually, so EDA is needed to automate design processes. HPC supports the computing requirements of AI and ML model training in EDA so that chip designs can be optimized using predictive analysis that forecasts real-world performance of chips that are under development.
With parallel processing and low-latency, high-throughput operations, HPC provides the foundation for training AI models needed in EDA to create simulations and verify design performance.
Designing Scalable Infrastructure for Increasing Design Demands
EDA workloads continue to grow in complexity as the demand for higher chip performance increases, meaning semiconductor companies must design supporting infrastructure that can scale. Scalable HPC architectures empower semiconductor companies to expand their compute and storage capacity along with their analytics capabilities without interrupting design workflows.
HPC scales through a modular, cluster-based design that allows engineers to add computing nodes, storage, and networking components to handle larger EDA workloads. High-speed, low latency interconnects maintain performance across thousands of processors.
Managing Performance Across EDA Compute Environments
Running high-performance EDA environments in engineering-heavy organizations like semiconductor companies that rely on multiple platforms creates operational complexity. HPC provides a core platform for unifying the management of EDA infrastructure. The advanced tools in HPC environments support job scheduling, workload distribution, and system optimization to maintain optimum performance and efficiency across clusters to keep critical chip design projects moving.
Partnering for Long-Term EDA HPC Success
Semiconductor companies benefit from working with specialists who understand both HPC architecture and the computation demands of EDA workloads for chip design. Re-Store has a successful track record of helping companies that use EDA to develop, implement, and optimize high-performance computing infrastructure for long-term design and performance innovation. We combine our expertise in EDA with our role as IBM's go-to partner since 2008 for architecting and operating HPC systems to customize HPC systems to meet the needs of each semiconductor company we work with.
Let's Simplify Your Supercomputing.
Get guidance, expertise, and operational support for your HPC environment demands from the team IBM trusts.
HPC in EDA FAQs
High performance computing in electronic design automation is used to run simulations, verify chip designs, and process large datasets required for semiconductor development.
Semiconductor companies use HPC for chip simulation, design verification, timing analysis, and processing large design datasets.
HPC allows engineering teams to run more simulations faster, improve design accuracy, and reduce time-to-market for new semiconductor products.
IBM provides scalable compute platforms, high-performance storage systems, and data management technologies that support complex EDA workloads.
Yes. HPC enables AI and machine learning models that help optimize chip design, improve performance, and automate parts of the design process.
Organizations should look for scalable infrastructure, high-performance storage, efficient workload management, and expertise in supporting complex design environments.