AI For Data Management

The quantity and sources of data remain challenging, but new approaches are being developed to deal with it.

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Data management is becoming a significant new challenge for the chip industry, as well as a brand new opportunity, as the amount of data collected at every step of design through manufacturing continues to grow.

Exacerbating the problem is the rising complexity of designs, many of which are highly customized and domain-specific at the leading edge, as well as increasing demands for reliability and traceability. There also is a growing focus on chiplets developed using different processes, including some from different foundries, and new materials such as glass substrates and ruthenium interconnects. On the design side, EDA and verification tools can generate terabytes of data on a weekly or even a daily basis, unlike in the past when this was largely done on a per-project basis.

While more data can be used to provide insights into processes and enable better designs, it’s an ongoing challenge to manage the current volumes being generated. The entire industry must rethink some well-proven methodologies and processes, as well as invest in a variety of new tools and approaches. At the same time, these changes are generating concern in an industry used to proceeding cautiously, one step at a time, based on silicon- and field-proven strategies. Increasingly, AI/ML is being added into design tools to identify anomalies and patterns in large data sets, and many of those tools are being regularly updated as algorithms are updated and new features are added, making it difficult to know exactly when and where to invest, which data to focus on, and with whom to share it.

“Every company has its own design flow, and almost every company has its own methodology around harvesting that data, or best practices about what reports should or should not be written out at what point,” said Rob Knoth, product management director in Cadence’s Digital & Signoff group. “There’s a death by 1,000 cuts that can happen in terms of just generating titanic volumes of data because, in general, disk space is cheap. People don’t think about it a lot, and they’ll just keep generating reports. The problem is that just because you’re generating reports doesn’t mean you’re using them.”

Fig. 1: Rising design complexity is driving increased need for data management. Source: IEEE Rising Stars 2022/Cadence

As with any problem in chip design, there is opportunity in figuring out a path forward. “You can always just not use the data, and then you’re back where you started,” said Tony Chan Carusone, CTO at Alphawave Semi. “The reason it becomes a problem for organizations is because they haven’t architected things from the beginning to be scalable, and therefore, to be able to handle all this data. Now, there’s an opportunity to leverage data, and it’s a different way. So it’s disruptive because you have to tear things apart, from re-architecting systems and processes to how you collect and store data, and organize it in order to take advantage of the opportunity.”

Darron May, Questa VIQ product manager, design verification technology at Siemens EDA also sees customers struggling with these issues because they have all of these individual systems.”The bigger customers are starting to develop data lakes to put all this information in, but then the question is what to store, and how to tag all of this type of information. It really is a massive challenge.”

Buckets of data, buckets of problems
The challenges that come with this influx of data can be divided into three buckets, said Jim Schultz, senior staff product manager at Synopsys. The first is figuring out what information is actually critical to keep. “If you make a run, designers tend to save that run because if they need to do a follow up run, they have some data there and they may go, ‘Okay, well, what’s the runtime? How long did that run take, because my manager is going to ask me what I think the runtime is going to be on the next project or the next iteration of the block. While that data may not be necessary, designers and engineers have a tendency to hang onto it anyway, just in case.”

The second challenge is that once the data starts to pour in, it doesn’t stop, raising questions about how to manage collection. And third, once the data is collected, how can it be put to best use?

“Data analytics have been around with other types of companies exploring different types of data analytics, but the differences are those are can be very generic solutions,” said Schultz. “What we need for our industry is going to be very specific data analytics. If I have a timing issue, I want you to help me pinpoint what the cause of that timing violation is. That’s very specific to what we do in EDA. When we talk about who is cutting through the noise, we don’t want data that’s just presented. We want the data that is what the designer most cares about.”

Data security
The sheer number of tools being used and companies and people involved along the design pathway raises another challenge — security.

“There’s a lot of thought and investment going into the security aspect of data, and just as much as the problem of what data to save and store is the type of security we have to have without hindering the user day-to-day,” said Simon Rance, director of product management at Keysight. “That’s becoming a bigger challenge. Things like the CHIPS Act and the geopolitical scenarios we have at the moment are compounding that problem because a lot of the companies that used to create all these devices by themselves are having to collaborate, even with companies in different regions of the globe.”

This requires a balancing act. “It’s almost like a recording studio where you have all these knobs and dials to fine tune it, to make sure we have security of the data,” said Rance. “But we’re also able to get the job done as smoothly and as easily as we can.”

Further complicating the security aspect is that designing chips is not a one-man job. As leading-edge chips become increasingly complex and heterogeneous, they can involve hundreds of people in multiple companies.

“An important thing to consider when you’re talking about big data and analytics is what you’re going to share and with whom you’re going to share it,” said Synopsys’ Schultz. “In particular, when you start bringing in and linking data from different sources, if you start bringing in data related to silicon performance, you don’t want everybody to have access to that data. So the whole security protocol is important.”

Even the mundane matters — having a ton of data makes it likely, at some point, that data will be moved.

“The more places the data has to be transferred to, the more delays,” said Rance. “The bigger the data set, the longer it takes to go from A to B. For example, a design team in the U.S. may be designing during the day. Then, another team in Singapore or Japan will pick up on that design in their time zone, but they’re across the world. So you’re going to have to sync the data back and forth between these kinds of design sites. The bigger the data, the harder to sync.”

Solutions
The first step toward solving the issue of too much data is figuring out what data is actually needed. Rance said his team has found success using smart algorithms that help figure out which data is essential, which in turn can help optimize storage and transfer times.

Siemens’ May said specific technology developed in direct response to the flood of data is allowing engineers and designers to easily access information that could otherwise be overlooked. “We have a number of applications that sit on this framework in the web browser,” he said. “We built common services on top of this, the server and then applications on top of those common services and there’s separate parts of the verification process. There’s test planning which is about putting together a plan for your verification and how you’re going to test things. And that’s really the kind of best guidance that you have for the whole verification process.”

There are less technical problems that can rear their heads, as well. Gina Jacobs, head of global communications and brand marketing at Arteris, said that engineers who use a set methodology — particularly those who are used to working on a problem by themselves and “brute forcing” a solution – also can find themselves overwhelmed by data.

“Engineers and designers can also switch jobs, taking with them institutional knowledge,” Jacobs said. “But all three problems can be solved with a single solution — having data stored in a standardized way that is easily accessible and sortable. It’s about taking data and requirements and specifications in different forms and then having it in the one place so that the different teams have access to it, and then being able to make changes so there is a single source of truth.”

Here, EDA design and data management tools are increasingly relying on artificial intelligence to help. Schultz forecasted a future where generative AI will touch every facet of chip development. “Along with that is the advanced data analytics that is able to mine all of that data you’ve been collecting, instead of going beyond the simple things that people have been doing, like predicting how long runtime is going to be or getting an idea what the performance is going to be,” he said. “Tools are going to be able to deal with all of that data and recognize trends much faster.”

Still, those all-encompassing AI tools, capable of complex analysis, are still years away. Cadence’s Knoth said he’s already encountered clients that are reluctant to bring it into the mix due to fears over the costs involved in disk space, compute resources, and licenses. Others, however, have been a bit more open-minded.

“Initially, AI can use a lot of processors to generate a lot of data because it’s doing a lot of things in parallel when it’s doing the inferencing, but it usually gets to the result faster and more predictably,” he said. So while a machine learning algorithm may generate even more vast amounts of data, on top of the piles currently available, “a good machine learning algorithm could be watching and smartly killing or restarting jobs where needed.”

As for the humans who are still an essential component to chip design, Alphawave’s Carusone said hardware engineers should take a page from lessons learned years ago from their counterparts in the software development world.

These include:

  • Having an organized and automated way to collect data, file it in a repository, and not do anything manually;
  • Developing ways to run verification and lab testing and everything in between in parallel, but with the data organized in a way that can be mined; and
  • Creating methods for rigorously checking in and out of different test cases that you want to consider.

“The big thing is you’ve got all this data collected, but then what is each of each of those files, each of those collections of data?” said Carusone. “What does that correspond to? What test conditions was that collected in? The software community dealt with that a while ago, and the hardware community also needs to have this under its belt, taking it to the next level and recognizing we really need to be able to do this en masse. We need to be able to have dozens of people work in parallel, collecting data and have it all on there. We can test a big collection of our designs in the lab without anyone having to touch a thing, and then also try refinements of the firmware, scale them out, then have all the data come in and be analyzed. Being able to have all that done in an automated way lets you track down and fix problems a lot more quickly.”

Conclusion
The influx of new tools used to analyze and test chip designs has increased productivity, but those designs come with additional considerations. Institutions and individual engineers and designers have never had access to so much data, but that data is of limited value if it’s not used effectively.

Strategies to properly store and order that data are essential. Some powerful tools are already in place to help do that, and the AI revolution promises to make even more powerful resources available to quickly cut down on the time needed to run tests and analyze the results.

For now, handling all that data remains a tricky balance, according to Cadence’s Knoth. “If this was an easy problem, it wouldn’t be a problem. Being able to communicate effectively, hierarchically — not just from a people management perspective, but also hierarchically from a chip and project management perspective — is difficult. The teams that do this well invest resources into that process, specifically the communication of top-down tightening of budgets or top-down floorplan constraints. These are important to think about because every engineer is looking at chip-level timing reports, but the problem that they’re trying to solve might not ever be visible. But if they have a report that says, ‘Here is your view of what your problems are to solve,’ you can make some very effective work.”

Further Reading
EDA Pushes Deeper Into AI
AI is both evolutionary and revolutionary, making it difficult to assess where and how it will be used, and what problems may crop up.
Optimizing EDA Cloud Hardware And Workloads
Algorithms written for GPUs can slice simulation time from weeks to hours, but not everything is optimized or benefits equally.



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