By Ron Collett

Underestimating the complexity of an SOC semiconductor design project is a growing problem in our industry. In an era where SOC projects cost tens of millions of dollars to complete, a week of schedule slip means $1 million or more in lost revenue potential. That’s unacceptable.

That was my main point last week during a panel I participated on that was part of the EE Times SOC Virtual Conference.

Former EE Times EDA Editor Richard Goering, now blogging for Cadence, captured the panel well in a post this week (Are SoC Development Costs Significantly Underestimated?).

To justify the investment in an SoC, Collett said, the available revenue stream must be 10X the development costs. Thus, if an SoC has a $500 million market opportunity, development costs should not exceed $50 million. Today, however, development costs can easily reach $40 to $80 million. Collett noted that 60 percent of this cost is labor and that the major part of the overall development cost is verification.

Richard, with a great comparison, went on to write:

Anyone who has ever been involved in a home remodeling project knows how hard it is to get a reliable estimate up front of how long it will take and how much it will cost. Underestimating time and cost is commonplace. A large SoC design project is far more complex, with many more stakeholders. There is no simple answer to the question of how development costs can be accurately predicted. But there are some ideas about how to lower development costs.

Tensilica CTO Grant Martin weighed in from the IP perspective, Xilinx VP of Product Development Steve Douglass offered the FPGA perspective, and ASIC designer Sven Andersson from Realtime Embedded AB talked about the value of verified IP blocks. It was a great conversation, and you can hear it in archived form by registering for the event.

There’s some additional information about the panel (we tweeted some highlights during the panel) that have been cataloged under the hash tag #eetsoc.And we’ve published a helpful white paper on how to measure IC development productivity in our online library.

Time really is money in the semiconductor industry, and quantifying schedule risk is an excellent way to maximize your engineering investments.

By Ron Collett

I had the pleasure of participating in a great online panel yesterday that was part of the EE Times SOC Virtual Conference, attended live by more than 1,500 people. CTO Grant Martin with Tensilica, product-development Vice President Steve Douglass with Xilinx and ASIC and FPGA designer Sven Andersson of Realtime Embedded AB all contributed to robust discussion of where next-generation design is headed.

I encourage you to listen to panel, which is now archived for the next six months.

My point was pretty straight forward:

• If you misunderstand your semiconductor design project’s true cost, your SOC may be doomed.

Think about it: An SOC design today needs to return 10x its investment. There aren’t a lot of huge end markets that justify SOC projects where the costs and schedule aren’t carefully managed. If the design costs $50 million to $80 million to develop, and there’s only a $200 million market, then the design can’t be justified.

So getting your arms around true development cost is what SOC development is all about.

By Ron Collett

Big changes are occurring before our eyes in the semiconductor world. And while you might say that the industry always has been in a state of flux, understanding the nature of today’s changes is key; reacting properly to that understanding is imperative.

What’s new? In short, it’s a shift in focus: The long transition toward the fabless model is almost complete. With the numbers of semiconductor companies doing their own manufacturing dwindling to a handful, the time has come for executives and engineering managers to figure out where their differentiation now lies within their companies.

Manufacturing used to be one of those differentiators. But today, with everyone buying manufacturing services from TSMC, UMC, Chartered or other foundries, there’s very little differentiation in how ICs are manufactured. But there can be enormous differentiation and value in how they’re designed.

How is this possible, in a world of well-established design-automation tools and methodologies? One approach is to bring more predictability and productivity to design projects and teams; to help engineering managers get insightful, relevant data early in the design decision-making process; and to enable a portfolio of designs to be centrally managed efficiently. That’s our business, and it’s a topic I’ll explore in detail this Wednesday (Sept. 16) during EE Times’ SoC Virtual Conference.

I’ll be presenting on a panel (Economics of Next-Generation SOC Design: A Node Too Far? 2-3 p.m. PDT) with Grant Martin, chief scientist, Tensilica; Steve Douglass, vice president, product development, Xilinx; and Sven Andersson, ASIC FPGA designer, Realtime Embedded AB. The panel will be moderated by EE Times’ Online Editor Dylan McGrath.

If you want a peek at some of what will inform my presentation, take a look at our Numetrics solutions page for starters. And then think about the implications of these two statistics:

* 60 percent of IC projects slip at least one quarter.

* 16 percent of IC projects slip more than one year.

I hope to see you live Wednesday during the virtual panel!

The phrase “top gun” generally refers to hot-shot fighter pilots performing amazing feats high in the sky, but increasingly it’s being used to describe great engineering managers doing amazing things on land. Numetrics has put together an online seminar covering the best practices of leading IC project planners.

The webinar describes eight techniques used by top-gun engineering managers, followed by a demonstration of Numetrics’ NMX-ERP™ solution and IC Industry Database containing more than 1400 completed IC designs from multiple industry segments.

The webinar presented the following best practices:

1. Computing IC complexity statistically
2. Estimation of resource requirements based on models
3. Rigorous “what-if” analysis for schedule / resource optimization
4. Benchmarking project execution assumptions
5. Determining the most aggressive, yet achievable project plan
6. Quantitatively assessing the schedule / resource implications of each feature request
7. Performing root-cause analysis at the project close milestone
8. Foreseeing resource shortfalls across the project pipeline.

The demonstration showed a live application of the Numetrics toolset, through a realistic scenario involving balancing IC specification and resource availability in the context of a fixed schedule.

You may view the webinar at http://techonline.stream57.com/numetrics/.

For more information, please e-mail info@numetrics.com

Summary: Schedule predictability is the art and science of determining the completion date for your semiconductor IC project, based on a statistical model, validated across multiple designs.

When you plan a project, you are working with incomplete information. Organizational changes, specification changes, technical challenges and more conspire to make it difficult to accurately predict when your new product will be ready.

Schedule predictability is the art and science of determining the completion date for your project, based on a statistical model, validated across multiple designs. The key ingredients are your design’s complexity, coupled with your resource plan. With these two inputs, Numetrics can significantly improve the accuracy of your schedule predictions. One customer went from consistent overruns to accuracy within a few percent on the first designs they modeled in the Numetrics toolset.

How is this possible? The core is the Numetrics ability:

• To understand which factors drive complexity
• To create a normalized characterization of your design that allows comparison with others.

When we compare your proposed design with historical productivity and schedule information, we can statistically determine the expected schedule for your new project. The accuracy of the model is enhanced by our industry database of over 1200 designs, coupled with specific information from your company’s historical project record.

The result is a robust, realistic prediction of the schedule, based on

• Complexity
• Resource availability and
• Historical data.

The value is a greatly enhanced ability to meet your market windows, time and time again.

Summary: Comparing the specifics of your semiconductor design with industry and company history enables insightful what-if scenario analysis to help manage development programs effectively .

During the planning stages for a chip design, there are a number of variables that can be tweaked in the creation of the final product plan. We can reduce or expand the feature set for a new device. We can reduce or extend the schedule. And we can reduce or increase the number of full-time-equivalent (FTE) staff allocated to the project. By manipulating these variables, we can negotiate a plan between the different stakeholders. In some cases, resources are the limiting factor. In others, the schedule is non-negotiable (for example a lot of consumer products must be ready for CES).

Running a lot of plans against all these variables has historically been difficult and time-consuming. In addition, the results have always been subject to arguments because there has been no trusted model to relate complexity, resources and schedule. Numetrics changes all that. By tweaking resource, schedule or feature set (complexity) assumptions, NMX-ERP can rapidly generate graphs that show the feasibility of each plan, and compare it with company and industry norms using their proprietary complexity engine and plan synthesizer.

The speed and defensibility of these analyses lends them great power. It is not rational to assume productivity or schedules that are significantly different from past performance, so any feasible plan must lie close to the lessons of history. There is a cost to adding features, or to shrinking the schedule, or to reducing headcount. The most effective way to negotiate these choices is with the aid of an objective toolset that combines the specifics of your design and plan with industry and company history. The tool is fast enough that you can run tens or even hundreds of plans in minutes or hours. From these scenarios you can then pick the plan that best meets your business goals.

Summary: Realistic semiconductor IC project planning hinges on industry-norm effort, which is the comparison of the actual productivity achieved by teams across the industry and the actual complexity of their designs.

Complexity is a measurement of how difficult it is to complete a design. It’s a measurement based on many attributes of the design, carefully correlated across multiple historical designs. But in order to plan, we need to know the amount of effort it will take to complete a design of a certain complexity. The answer lies in a comparison of the actual productivity achieved by teams across the industry and the actual complexity of their designs.

From this comparison we can calculate the amount of complexity an average designer can implement in a unit of time. Because this is a normative value calculated across the industry, we call it industry norm effort.

We can also make the same calculation for your company—assessing the amount of complexity your designers have historically been able to implement in a unit time. By comparing this with the industry norm, you will get a sense of how your team is doing as compared with the industry.

But the main use of industry norm effort is in conjunction with the complexity data for a proposed design:

• We can accurately and rapidly calculate the total effort required for that design using either your company data, or the industry norm data.

This provides a firm foundation for realistic planning, while still allowing you to set aggressive (but not unrealistic) targets for your team.

Summary: Design complexity can be quantified and communicated in a way that makes IC projects predictable and more productive.

It doesn’t take a rocket scientist to know that the resources and time required to build a chip vary from one design to another. The variation is a function of how difficult the chip will be to build. We measure that and call it complexity.

The hard part, however, is to know which attributes of the design correlate to the effort required to build the chip.

• Is clock speed important?
• What about the number of transistors?
• Re-use?
• Analog and mixed signal?
• Voltage islands?
• Modes?

The list goes on and on. One of the reasons why the NMX-ERP™ software suite accurately forecasts the time and resource requirements for a design is that our engineers, using more than a thousand design projects, have developed a deep understanding of just how hard a given project may be so your engineers can be more productive.

Knowing how to translate chip-design attributes into complexity is the foundation of apples-to-apples comparisons between designs. That’s critical to making sure your latest design can be compared with other industry designs, as well as designs your company has done in the past. After taking in all the complexity factors as chip specifications, Numetrics’ engines can reliably and rapidly calculate the relative complexity of your design, as compared with every other design in our industry database. That’s the foundation upon which all the plan synthesis, what-if scenario analysis, re-planning and root-cause analysis capabilities of NMX-ERP are built.