I Have Seen The Future

Schedule and time to market topped the business challenges in a new survey, while design complexity and performance topped the technical challenges.


We recently concluded an online survey that measured design challenges and general sentiment regarding how they can be addressed, with some specific forward-looking queries. The title of the survey was “Big Data, the Cloud and Internet of (Silicon) Things.” We essentially asked our survey respondents to look into the future. We got an excellent response to this survey, with lots of thoughtful commentary. I do feel I’ve now seen the future after reading all those comments.

Before we get into specifics, I’d like to pause on that phrase, “I have seen the future” for a moment. Some of you may recall the 1964/1965 World’s Fair that was held in Queens, New York City. I certainly do. General Motors had a pavillion there that showcased future innovations. A lot of companies did that. Their slogan was “I have seen the future”, and lot of folks walked around wearing buttons that said that. A long time ago, I was digging in my parent’s attic, and I discovered the General Motors button from the 1939 New York World’s Fair. You guessed it, “I have seen the future” was their slogan then, too. Nice consistency. I’ll examine how good GM’s future predictions were at another time.

Figure 1

We committed to not publish survey details, so I’ll give you some general trends here. Recent hacks to iCloud notwithstanding, there has always been a general distrust to put design data in the cloud. This survey confirmed that. Personal wealth, “sensitive” photos – no problem. Design RTL in the cloud, NO WAY!

Schedule and time to market topped the business challenges in the survey – by a comfortable margin. Design complexity and performance topped the technical challenges list.

I wanted to spend just a moment on the third topic mentioned in our survey title, Big Data. There is a common misperception that Big Data means analyzing massive amounts of information using unconventional methods, since conventional methods break when data sets get too large. This is a partial, but not complete definition of what the term means. Generally, you can think of the topic as consisting of four dimensions:


  • Volume: Size of data, the easy one
  • Velocity: How often the data changes
  • Variety: The number of different sources of data
  • Veracity: The certainty of the information

Furthermore, it’s important to understand why we talk about Big Data in the first place – to discover hidden patterns, correlations and other useful information. When you consider this broader definition of Big Data, I believe you will start to see how it might fit the IC design problem. Do we know how to look at all past design performance and predict what will happen on the next design? A lot of folks in the survey felt the answer today is “no”, but they really want it be “yes”. Maybe 20/20 forward vision is what the future will look like.

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