By Ron Collett

The concept seems simple: The more ip blocks you re-use in an IC or system design, the faster and more productively you’ll get your design done. The ITRS roadmap began identifying the benefits as long ago as 1997, showing the industry could reasonably expect 56,000 gates per designer per year when using large ip blocks (75,000-1 million gates). By 2007 that figure was up to 600,000 gates per designer per year, a tenfold increase.

There’s no doubt design reuse is here to stay. In 2007, a third of all logic was reused design blocks. That’s expected to rise to nearly 50 percent by 2015, according to the ITRS.

The numbers and the theory behind it are encouraging, but reality is much different. Making 30 percent of your design from reused IP blocks doesn’t mean you’re going to be 30 percent more productive at the end of the project. That’s because IC design teams tend to underestimate the work needed to implement the reused IP. This can cause project delays and missed market opportunities.

The challenge is that it’s very difficult to estimate design complexity, especially the impact of reuse. IC design schedules can falter because of the inability to estimate the impact of IP modifications on project effort.

Even a small percentage of reuse can add outsized effort to a development project. For example, if you add one new block of 600,000 gates to a 6 million-gate design, you’re adding 10 percent to the IC but increasing the effort required on the project by 24 percent. Adding 10 percent new circuitry to all blocks in that 6 million-gate design—with 90 percent of each block being re-used—doubles the effort required on the project, even though it increases the IC size by just 10 percent to 6.6 million gates.

This issue will be part of a larger discussion Dec. 1 at IP-ESC 2009 in Grenoble. We were invited to sit on a panel—“IP Reuse vs. IP Leverage: What’s the difference, and what are the issues?”—with Kathryn Kranen, CEO of Jasper Design Automation, and Olivier Haller, who manages the design verification team in the Functional Verification Group at STMicroelectronics. Our director of professional services, Andrea Fortunato, will represent Numetrics.

This is a well-timed panel in my opinion because re-use is an issue that transcends the industry and is crucial for its future. And how we go about optimizing design re-use is crucial to manage today.

By Ron Collett

(Summary: As the semiconductor industry emerges from the recession, new ways of thinking are emerging as well to improve what’s becoming a new differentiator for companies: IC design development.)

All indications are the semiconductor industry is rebounding from the painful recession of the past couple of years. The latest upbeat data points include:

• Worldwide third-quarter PC microprocessor unit shipments rose 23% compared to the second quarter, reaching a new all-time high, according to market research firm International Data Corp. (IDC).
• Chip-sales growth should be 10 percent in 2010 and 8.4 percent in 2011, according to the Semiconductor Industry Association. The decline in 2009 chip sales (down 11.6 percent is now less that earlier forecast).
• Individually, companies like Marvell, TSMC and ON Semiconductor are reporting encouraging results.

But, as they say, there’s good news and bad news. The good news is obvious. The bad news is more subtle: Companies are beginning to crank up the product-development dial significantly, and this can become a challenge for R&D organizations.

As a surge of new projects occurs, hiring generally is slow to catch up to demand. This puts stress on engineering organizations. Schedules are difficult to predict, and the engineers can get shifted from one product development team to another in the race to make deadlines. Managing a portfolio of products turns into a torch-juggling exercise—spectacular to watch but done with the knowledge that the risk is high.

This is a significant problem in the fables era—a time in which IC design development is an increasingly important source of differentiation for semiconductor companies. A sudden burst of product-development activity can bring R&D organizations to their knees.

Design development productivity is something to consider as we emerge from this recession. The stakes are high, and there’s little room for error in marshalling engineering resources to get products to market quickly.

All recessions force change on business, and this one is no exception. Old ways of doing things are being replaced by new thinking on productivity—all with an eye toward making “up and to the right” last.

By Alex Silbey

(Summary: We inevitably get questions about Numetrics’ technology after webinars or live event presentations, and we’d like to share some of them in the spirit of helping you understand more about our products and solutions. Here are answers to several recent questions in the virtual mail bag).

Q: How do you define productivity?

A: We calculate complexity of the project and we divide the complexity units by total number of person weeks required to get that product out to volume production. That quotient gives you the productivity number. The typical range is 500 on the low end for a large team to 3000 for a small team.

There’s another measure, which is throughput, and throughput is complexity units per week. That’s a measure of normalized cycle team. Productivity is efficiency of the team and higher number is better.

Q: I’ve heard that in some sectors productivity decreases as team size increases. Is this true in semiconductor product development?

A: It’s a universal effect across pretty much any activity that has to do with building things. When you build larger teams, each person is doing a smaller and smaller slice of the overall work. More work has to be split apart and then put back together. Bigger teams equal more meetings and more management required. It’s universal and it’s inevitable. With the Numetrics approach, you can minimize this effect—decreasing productivity curve is flatter than it would otherwise be.

Q: It’s impossible to predict in a design project how many times customer requirements will change, when your EDA tools go buggy or if a key contributor leaves the team. So how do you quantify schedule risk with so many unpredictable variables?

A: The simple answer is our tools don’t predict things. You have a draw a line between statistical analysis and a crystal ball.

What Numetrics’ tools do is take your inputs of design parameters and measure them against the history of more than 1,500 design projects over eight generations of technology evolution (here’s a link to a demo of our tools). Using the data from those hundreds and hundreds of designs, this builds in realistic effort required to deal with those issues. It’s a way of contingency planning.

Think of it like yield modeling. You know that on each wafer a certain number of dice will fall out. Yield modeling doesn’t tell you which particle is going to hit which die and where. But they give you an accurate assessment of how your design will yield. Numetrics is like a yield model for project plans. It’s saying there’s a certain probability that if you’re going to try to achieve these targets, given what you’ve input you’re going to fail.

It allows you to make a quantitative assessments. It’s a probability model. It’s not a crystal ball.

Q: How does the complexity calculation model handle predictions for newer nodes, such as 45 and 32nm?

A: Numetrics’ IC Industry Database has collected information for eight technology generations. The technology shifts from one generation to another have been observed before. And what we’ve observed is that early users of technology nodes face considerably more complexity than later users of the same node, once the models and such are more stable. The equation has calibrated this effect which repeats from generation to generation. We’ve been able to model what the effect of the extra technology of a new node will be on a new design.

Q: Can your tools get data from existing sources or do I have to input it manually?

A: We’re dealing with milestones, staffing information and complexity information. Typically this information is copy-pasted from existing sources or customers are using XML import to get data into our tools.

(Alex is Numetrics’ director of professional services).