EDA Challenges and How Pure Helps Solve Them |

The global semiconductor industry is growing rapidly. Sales reached $64.9 billion in August 2025, an increase of 21.7% year-over-year, and in 2024 sales were $627 billion for the year. The massive proliferation of generative AI and related use of large language models (LLM) is the primary driver of this growth.

But now, chip shortages are forcing manufacturers to speed up electronic design automation (EDA) workloads to accelerate the chip design process. High-performance storage can dramatically accelerate chip design and ultimately enable delivery of new semiconductor products faster.

Read on to learn more about what EDA is, why it’s important, the challenges EDA-based semiconductor chip manufacturers are facing, and the various ways they can address these challenges via modern data storage and data management.

What Is EDA?

EDA, also known as electronic computer-aided design (ECAD), is a category of software, hardware, and services used for manufacturing, analyzing, and testing semiconductor chips. EDA tools make it much easier to design semiconductor chips, which often have billions of components and keep adding more as Moore’s Law takes effect.

Why Are EDA Solutions Important?

EDA tools play a critical role in semiconductor chip manufacturing for the following reasons:

They’re used to vet semiconductor manufacturing processes to ensure they deliver the required performance and density. This part of EDA is called technology computer-aided design (TCAD).
EDA solutions also verify that a design can meet all manufacturing process requirements. Design deficiencies can lead to reliability risk, reduced capacity, and malfunctioning.
EDA tools are also used to monitor the post-manufacturing performance of semiconductor chips to ensure they continue to perform as expected throughout their lifetime.

EDA Vendor Challenges

EDA tools and solutions are heavily reliant on data to function well. Like all other data-related tools, EDA solutions face challenges that come with having more and more data to deal with and faster data processing requirements. These issues translate into resource and cost issues, which eventually translate into slow, failed, or scrapped initiatives.

Specifically, EDA vendors face challenges around:

Time to market. Dev cycles are speeding up and many EDA vendors find themselves unable to keep up with competitors. Their builds and regressions are taking too long.
Management complexity and its ensuing lack of productivity. IT teams are very resource-constrained and need to keep up with chip designer demands.
Increasing costs from application infrastructures that still run on legacy storage. IT departments are spending unnecessarily as their infrastructure gets over-provisioned to achieve higher scalability.
Massive growth in compute resources during the design and manufacturing process, which has become a major source of investment. Sub-10nm chip design is now the standard. This process generates a massive amount of files during different phases of the workflow, and these files require high-performance and high-capacity data storage. Verification and simulation job completion times have become unpredictable. SSD-based storage cannot meet the requirements for running workloads with low latencies at scale.

Considering all of this, it’s no surprise that EDA vendors and manufacturers are struggling with their data-related costs and processes.

Why EDA Workloads are Challenging from a Storage Perspective

Here are some of the storage pain-points that EDA workflows typify:

EDA front-end/design phases have huge numbers of small files, many metadata operations (creates, stats, opens) as project workspaces are heavily accessed concurrently.
Many EDA workflows involve shared workspaces or simulation farms where many compute nodes/engines access the same file trees and directories concurrently – high concurrency, heavy metadata, directory operations.
The back-end phases (sign-off, simulation, physical design) may have large files, large data sets, high throughput needs (for example during tape-out prep) plus high I/O.
Storage bottlenecks means delays in simulations or verification, meaning slower flows, idle compute, wasted licenses.
Because of the small-file/metadata bias and concurrency, the storage system must handle not just raw throughput (MB/sec) but metadata IOPS, scalability of namespace, many files, directories, fault tolerance, shared namespace. For example, one vendor noted that in EDA builds, metadata operations can dominate.

All of the above is forcing EDA vendors to rethink how they do things.

They’re wondering how to:

…accelerate their builds, regressions, and tapeout cycles?

…run more EDA workloads in parallel without over-provisioning storage or compromising performance?

…manage scale and systemic complexities in their EDA environment for designing and taping out sub-10nm?

…run EDA workloads (build, simulation, verification, testing, and tapeout) requiring high IOPS and bandwidth with low latency?

…run multiple design or tapeout projects simultaneously on the same infrastructure with predictable outcomes?

…get 0.06 milliseconds response time for metadata operations at scale?

…optimize EDA tool license utilization and lower costs by achieving faster job completion time and accelerated onboarding of new projects?

…keep shared data, such as libraries, in a project directory and further speed up complete design workflows for multiple tapeouts?

…keep costs lower with modular architecture as pods and maximize performance per rack space and reduce carbon footprint?

…leverage storage-aware job scheduling to avoid failures or slow down of jobs submitted in the queue?

The answer: Pure Storage, which can do all of the above.

How Pure Storage Helps EDA Vendors

Traditional storage architectures simply can’t keep up with the I/O intensity, metadata churn, and concurrency of modern EDA workloads. As chip complexity and simulation scale explode, legacy systems either saturate or scale inefficiently. EDA tools rely on high-performance computing (HPC) environments that run thousands of parallel jobs 24×7—requiring infrastructure that can dynamically scale to feed compute nodes and accelerate time to market.

Pure Storage® FlashBlade//EXA™ redefines what’s possible for semiconductor design and verification. Purpose-built for exascale performance, FlashBlade//EXA delivers multi-terabyte-per-second throughput and disaggregated metadata scaling—enabling teams to run more builds, regressions, and tapeouts in parallel with ultra-low latency. Its independent scaling of metadata and data ensures consistent performance across millions of small files and large simulation outputs alike.

The result: faster design closure, higher license utilization, and simpler scaling. FlashBlade//EXA’s modular, all-flash architecture eliminates over-provisioning while maximizing performance per rack unit. With unified observability, hybrid cloud connectivity, and REST APIs for automated workflow integration, FlashBlade//EXA helps EDA vendors reduce total cost, accelerate every phase of chip design, and ensure that storage never becomes the bottleneck to innovation.

Learn more about how FlashBlade can accelerate EDA workflows.

Proof Point: Silicon Labs

Silicon Labs manufactures electronic chips that drive remote-controlled light bulbs, smart home systems, and industrial automation processes. Their EDA procedure used for chip creation generates numerous small files and directories for each project. EDA workloads, which heavily rely on metadata, pose a data throughput challenge for outdated storage systems. To address this challenge, Silicon Labs turned to Pure Storage FlashBlade® for high-performance data storage, not only for EDA but also for various other workloads.

Silicon Labs required storage solutions that could accelerate their design process, efficiently managing large volumes of unstructured data. Concurrently, they aimed to streamline their other workloads onto a unified system for enhanced efficiency and simplicity. With multiple locations, including some lacking in-house IT support, Silicon Labs needed a user-friendly solution from a vendor offering robust support.

Using Pure Storage, Silicon Labs was able to:

Enhance innovation speed through high performance and reliability.
Streamline workloads on a single platform while boosting overall performance.
Achieve speeds up to 40 times faster for certain functions compared to legacy HPC storage.
Reduce data center operations footprint by 85%, simplifying operations.

Other Proven Results

Here are other results EDA-industry players have achieved with Pure Storage:

A major EDA provider saw a 10-time build run-time improvement and twice the number of builds.
A top-10 semiconductor manufacturer can now enable 200 million more simulations every year and reached 70GB/s and 2.3 million IOPS.
A major EDA provider saw a 40% Questa regression runtime improvement.
EDA companies have sped up millions of file operations up to 20x with RapidFile Toolkit.

Ready to change your EDA game? See why FlashBlade//S is the ideal storage solution for EDA workloads.