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

By Eric
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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.

Modularity as the Recipe for Unique Products

By Olivier
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Diversification is a popular word these days. It contrasts with standard, which does not inspire the memorable experience we seek from our products. “To buy or not to buy”, that is definitely the question. We consumers want to buy products that provide us with the most delightful experiences. This is one of the reasons brands constantly seek ways to satisfy our quest for uniqueness. Why have what everyone else has when we can choose something unique? Unique feature, unique design, favorite color. The reality is that demand for diversity is on the upswing.

But wait a minute. How many product variants do Industrial Equipment (IE) companies have to manage without sacrificing margin and ultimately their longevity? Just how many types of products do they have to produce to satisfy different customer preferences? These are complicated questions for companies still struggling with an Engineered To Order (ETO) approach that has them spending too many hours developing individual products. It is simply not sustainable.

There are, however, companies that have recognized the need to switch gears, from an Engineered To Order approach to a Configured To Order (CTO) philosophy. And they are doing this by embracing modularity. You will want to read the interview of Alex von Yxkull, president and CEO of Modular Management, and Johan Källgren, partner, from our first issue of Compass magazine, to understand the challenges and rewards of modularization.  If you prefer a live explanation, check out the video below in which Colin de Kwant, a consultant from Modular Management explains that in a world in which we want it all, customization forces manufacturers to address two conflicting objectives – simplify complexity and amplify variety. The companies with an ETO approach risk failing to deliver.

How to switch from ETO to CTO? Watch this:

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Modularity is the answer for IE companies that are looking for ways to ensure that product assortment meets customer requirements without inflating the number of managed parts. By reorganizing and rethinking the way products are developed and using common and interchangeable modules with predefined variants, modularity can pave the way to more innovation and timely delivery. 3DEXPERIENCE’s Simple Solution Selection helps IE companies diversify their product offering while minimizing the cost of complexity.

Keep your ears open; you haven’t heard the last of modularity… ;)

 

Expanding 3DEXPERIENCE to Nature with GEOVIA

By Aurelien
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Wow. If you saw our news earlier today, we had two major announcements:

  1. The first one is about our acquisition of Gemcom Software, the world leader in geological modeling and simulation for the mining industry.
  2. The second is the launch of our latest Brand dubbed GEOVIA. GEOVIA will be dedicated to the virtualization and simulation of nature. In other words, while you know CATIA as the Brand for “Virtual Product”, GEOVIA will be the Brand for “Virtual Planet”. Gemcom Software is, in fact, the first step towards this new endeavor.

We will have plenty of opportunities to further discuss about GEOVIA on this blog, but first things first, we’d like to wish a warm welcome to the 360 employees of Gemcom Software!  :-)

If you’re curious about Gemcom, you will want to listen to CEO Rick Moignard, talking about the company, its products, and how he sees fit with Dassault Systèmes’ 3D Experience platform. As I had the chance to talk with Rick recently, I really can tell how he is thrilled by the expanding capabilities in workforce safety and sustainability brought by the combination of our respective pieces of software.
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To learn more about Gemcom and its products, you will also want to check this out:

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Last but not least, if you’d like to follow Gemcom on social networks, here are direct links:

What do you think about this new milestone towards virtualization of Nature? What would you like to discuss more about? Please feel free to leave a comment.

 



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