Why Design Data Management and Analytics aren’t just ordinary Big Data

By Eric

SemiconductorsI’m an electronics engineer who spent a part of his career in the business intelligence and analytics domain. In that regard, I’m always interested in technology and business areas that have unique analytics needs. Semiconductor design closure is one such domain. With 14 nanometer geometry fabrication now coming on-line, the complexity of integrated circuits is taking another geometric step in complexity as large projects can have 200+ IP blocks in their designs (see figure below).

Variability and Velocity are more critical than Volume

When taking into consideration that millions of transistors can constitute a block and that blocks can be chosen from libraries in the thousands, and that there can be multiple variations of a block, the analytics challenge approaches that of Big Data. Though, this not necessarily because of overall data size, but because of data complexity, variability and velocity.

For these large projects, then, the effort to meet timing, power, IR drop and other design parameters takes geometrically longer…yet again. Of course, some of this increased verification effort can be done in parallel by multiple design teams, each working on sub-sections of the chip. But, ultimately the entire system design has to be simulated to assure right design first time. I’m sure most would agree with me that system failure often happens at interfaces. Whether it’s an interface within a design or a responsibility interface between designers, it’s the same situation.

Why ordinary Big Data analytics won’t do the job

Effective analytics for design testing and verification provides a way to analyze interface operation from all relevant perspectives. Coming back to the topic of Big Data, my view is that commonly known Big Data analytics tools could be helpful, but are not sufficient to meet this requirement. In particular, I observe that appropriate semiconductor big data analytics must have the following capabilities:

  • Support for the hierarchical nature of chip design.
  • Ability to integrate information from multiple design tools and relate them in some way to each other to indicate relevant cause/effect relationships.
  • The ability to compare and contrast these relationships using graphical analytics to expose key relationships super quickly.
  • The ability to easily zoom, pivot, filter, sort, rank and do other kinds of analytics tasks on data to gain the right viewpoints.
  • The ability to deliver these analytics with minimal application admin or usage effort.
  • Effective visualizations for key design attributes unique to semiconductor projects.
  • The ability to process data from analog, digital and the other types of common EE design and simulation tools.
  • The ability to handle very complex, large chip design data structures so that requirement, specification and simulation consistency is maintained.

It seems to me that semiconductor design engineers have been quietly contending with Big Data analytics challenges even though they haven’t necessarily been part of the mainstream Big Data conversations. Yet, the tools in use for chip design perhaps have some very interesting capabilities for other technical and business disciplines. My $.02.

Also, we’re going to be at the Design Automation Conference in San Francisco this year again. We will have a full presentation and demo agenda, a cocktail hour and prizes, join us!

Eric ROGGE is a member of the High-Tech Industry team. You can find him on Twitter @EricAt3DS.

Do You Comply?

By Kate


I imagine I’d go crazy if I were a manufacturer today. There are so many regulations to follow, and with the burgeoning environmental/green standards, which can differ per country, the complexity grows. Then, when I begin to think about the various substances that are regulated, like lead, hazardous chemicals, etc., coupled with the specific industry regulations, I start feeling like I need a Business Intelligence solution to understand it all. (Breathe now.) And then, I imagine how extra-complex it must be for OEMs and Tier 1 suppliers who are outsourcing their parts manufacturing around the globe, where depending on the country, the manufacturing cultures differ, and thus their awareness/compliance to the complex web of. . . directives.

Welcome to the second post in our introductory Green PLM blog series.

We can quickly get overwhelmed when we start digging into Compliance. When it comes to Green Compliance, we’re still in the early days, i.e. there’s a lot more to come. Most of the directives bubble up from Europe, and to my knowledge, so far there are no widespread, ISO-type standards.

Mike Zepp, our in-house regulatory compliance expert, used to deal directly with the type of scenario I imagined above, and now he helps Dassault Systèmes arm companies with tools to successfully navigate through the green compliance jungle. Mike was in Paris recently and kindly agreed to let me video-interview him. Here’s what Mike has to say about Compliance and the role PLM, particularly managing product-linked data throughout the lifecycle, can play to help. The real-life example he cites in the video is particularly telling:

It seems to me that using an efficient compliance assessment and impact analysis data management tool will help put some greenbacks into your Green PLM, or at least save you some. While this is only a piece of Green PLM, it’s a major one.

Stay tuned for my next Green PLM post on reducing material use in product design.

Best,

Kate

P.S. Here are some Green Compliance resources:

Examples of product recycling directives:
End-of-Life Vehicle (ELV)
Waste Electrical and Electronic Equipment Directive (WEEE)

Examples of banned substances directives:
Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Directive
Restriction of Hazardous Substances (RoHS)

Whitepaper:
The Voice of the Customer: Process Integration and Traceability Through Requirements Management



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