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Plunify’s InTime helps FPGA design engineers meet timing and area goals!


Kirvy Teo

Kirvy Teo

Engineers designing FPGA applications face many challenges. Using Plunify’s automation and analysis platform, engineers can run 100 times more builds, analyze a larger set of builds and quickly zoom in on better quality results. Using data analytics and the cloud, Plunify created new capabilities for FPGA design, with InTime being an example.

Kirvy Teo said: What happens when you need to close timing in FPGA design and still can’t get it to work? Here is a new way to solve that problem – machine learning and analytics. InTime is an expert software that helps FPGA design engineers meet timing and area goals by recommending “strategies”. Strategies are combination of settings found in the existing FPGA software. With more than 70 settings available in the FPGA software, no sane FPGA design engineer have the time or capacity to understand how these affect the design outcomes.

One of the common methods now is to try random bruteforce using seeds. This is a one-way street. If you get to your desired result, great! If not, you would have wasted a bunch of time running builds with you none the wiser. Another aspect of running seeds is that the variance of the results is usually not very big, meaning you can’t run seeds on a design with bad timing scores.

However, using InTime, all builds become part of the data that we used to recommend strategies that can give you better results, using machine learning and predictive analytics. This means you will definitely get a better answer at the end of the day, and we have seen 40 percent performance improvements on designs!

How has Plunify been doing this year so far? According to Teo, Plunify did a controlled release to selected customers in first quarter of 2014, who are mainly based in China. It is easier to guess who as we nicknamed them “BCC” – Big Chinese Corporations.

Unsurprisingly, they have different methodologies to solving timing problems and design guidelines, many of which were done to pre-empt timing problems at the later stage of the design. InTime was a great way to help them to achieve their performance targets without disrupting their tool flows.

Plunify is announcing the launch of InTime during DAC and will be looking to partner with sale organizations in US.

What’s the future path likely to be? Teo added: “Machine learning and predictive analytics are one of the hottest topics and we have yet seen it being used much in chip design. We see a lot of potential in this sector. Beyond what InTime is doing now, there are still many chip design problems that can be solved with similar techniques.

“First, there is a need to determine the type of problems that can be solved with these techniques. Second, we are re-looking at existing design problems and wondering, if I can throw 100 or 1000 machines to this problem, can I get a better result? Third, how to get that better result without even running it!

“As you know, we do offer a FPGA cloud platform on Amazon. One of the most surprising observations is that people do not know how to use all those cheap power in the cloud! FPGA design is still confined to a single machine for daily work, like email. Even if I give you 100 machines, you don’t know how to check your emails faster! We see the same thing, the only method they know is to run seeds. InTime is what they need to make use of all these resources intelligently.

Why would FPGA providers take up the solution?

The InTime software works as a desktop software which can be installed in internal data centers or desktops. It is on longer just a cloud play. It works with the current  in-house FPGA software that the customer already own. We are helping FPGA providers like Xilinx or Altera, by helping their customers with the designs. They will feel: How about “Getting better results without touching your RTL code!”

FPGA design heads to the cloud!

December 6, 2012 3 comments

Han Hua Ng

Harn Hua Ng

Singapore based Plunify claims that chip design companies can design faster and better using cloud computing. Stressing on the company’s go-to-market strategy, Plunify’s founder, Harn Hua Ng, said the Plunify partners with tool vendors, their distributors and complementary sales representatives.

Since pay-as-you-go business models are rare in the semiconductor industry, we went through several steps, of which the first was to better understand the market, the available tools and stake-holders:
* How is the market reacting to cloud computing and licensing schemes?
* What are current tool capabilities with regards to multiple CPUs/servers? Which parts of the chip design workflow can best take advantage of scalable, parallel features?
* What tools are more suitable for a cloud environment?

With these in mind, the next step was to build the cloud platform and the application clients to address immediate concerns – security, accessibility and cost.

“Then, we partner with tool vendors, their distributors and sales reps to bring our solutions to end-users. Companies of different sizes Plunify
view the advantages of cloud computing differently, so solutions need to be customized accordingly. Some see Plunify as solving longer term IT problems of scaling and provisioning; while others use us as an immediate way to speed up their design workflows. We are still in the process of learning about the market.”

How can the on-demand cloud computing platform dramatically accelerate chip design workflows? According to Harn Hua Ng, one  immediate benefit is an almost instantaneous fulfillment of peak demand IT requirements, for example, a urgent request to do 100 synthesis builds to fix a problem due yesterday. Or if the problem cannot be fixed, at least the design team will find out in a day rather than potentially in three months’ worth of runtime without a cloud solution. The longer term acceleration is a gradual parallelization of the design workflow.

Currently, chip designers tend to visualize the design workflow as a chain of mostly serial steps with many dependencies, just because many steps can be time-consuming (both in terms of runtime and time taken to analyze intermediate results).

With an on-demand compute platform, designers can have more room to experiment and to optimize, more readily incorporating agile practices in hardware development.
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