Chip shortages are compelling manufacturers to speed up EDA workloads to accelerate the chip design process. High-performance storage optimal for EDA workloads can dramatically accelerate chip design and ultimately enable the delivery of new semiconductor products faster.

Electronic design automation (EDA), commonly referred to as electronic computer-aided design (ECAD), is a group of tools for creating, examining, and testing semiconductor chips. EDA tools greatly simplify the design of semiconductor devices, which frequently contain billions of components and continue to do so as Moore's Law takes hold.

EDA tools are essential to the production of semiconductor chips for the following reasons: To make sure they give the appropriate performance and density, they are used to test semiconductor production processes. The term "technology computer-aided design" (TCAD) refers to this aspect of EDA. EDA tools also confirm that a design can adhere to all specifications for the manufacturing process. Design flaws put reliability at risk and cause capacity reductions and malfunctions. To make sure semiconductor chips work as intended throughout the whole of their lifetime, EDA tools are also used to monitor the performance of semiconductor chips after they have been manufactured.

Data is essential for the efficient operation of EDA tools and solutions. EDA solutions, like all other data-related tools, encounter difficulties brought on by the need to analyse data more quickly and the ever-increasing amount of data available. These problems result in problems with costs and resources, which ultimately result in efforts that move slowly, fail, or are abandoned. The I/O requirements of current EDA workloads are difficult for traditional storage systems to keep up with. Under the demands of high concurrency, they either saturate or scale poorly. To run jobs in a 24-hour queue for design and production processes, high-performance computing (HPC) workloads used by EDA tools need a significant amount of computational resources. To speed up the chip design process and reduce time to market, semiconductor organisations must be able to provision infrastructure on demand.

Accelerated chip design assists in streamlining the semiconductor design process to reduce errors and hasten time to market. Moreover, it improves concurrent work completion rates and computes density. Improved software developer productivity, more business and operational benefits, enhanced infrastructure efficiency, and integrated design tools are all available with faster agile development. Using disaggregated storage and computing can maximize the computing and storage investments, rendering a much higher ratio of IOP to capacity. Compared to competitors who use storage-class memory, which has a limited scaling range and is substantially more expensive, the read performance will be significantly higher and much more efficient. 

With Microsoft Azure's powerful HPC capabilities, flexible hybrid cloud architecture, cost-effective data reduction, faster data recovery from failures, optimised EDA tool licence costs, and data security and sovereignty with a connected storage hybrid cloud solution, engineers can work more productively and efficiently. Cost savings and increased productivity can be achieved by optimising the licencing of EDA software and by automating the provisioning and integration of EDA workflows utilising REST APIs. With the help of integrations and observability, it is possible to monitor and report on all endpoints from a single window while also connecting to monitoring tools like Prometheus and Datadog and retaining metrics data over time.

Basic ML approaches have accuracy problems that prevent them from being used for the majority of chip design activities. Chip designers need reliable data to build and verify chips. When compared to brute-force analysis, it might be small, function well in one situation, and save a lot of time and resources. It is vitally essential that chip designers use trustworthy data to make design decisions, so uncertainty about accuracy leads them to turn to brute-force approaches instead of machine-learning systems. Even though they may take longer to execute and only cover a smaller area of design space, brute-force techniques may still be more accurate.

Today's most popular machine learning techniques are supervised learning techniques, which are based on vast volumes of data that include well-known good design features. These are unlikely to be very helpful for chip designers because the specifics of what constitutes "excellent" alter quickly depending on the semiconductor process node, the company, and even the sort of design. Unsupervised techniques are very likely to be used successfully to raise manufacturing yield.

Designers can make reliable engineering judgements and defend those decisions in design reviews if they have good accurate results, the level of accuracy is known, and the machine learning predictions are verified. That might prevent the strategies from being employed on some of the more front-end tasks where the level of predictor error is still significant. Advanced nodes are becoming more complex due to design guidelines and other additional variables. Examples include, among others, the expanded back-biasing for FD-SOI, the various extraction conditions brought on by double patterning, and the wider voltage domain for finFETs, which requires considering 7–10 voltages as opposed to 3 for older nodes. A typical process, voltage, and temperature (PVT) space at legacy nodes may only have three process conditions, three voltages, and three temperatures, however, at current nodes, it's customary to run 50 to 100 PVT and extraction conditions.

 The quality of the results from machine learning depends on the data utilised to develop the design. The design must be included in the context of use cases and systems for it to be genuinely effective.