By Chris Rowen
Last night, I turned the last page on Walter Isaacson’s biography of the late Steve Jobs. Isaacson’s masterful work does remarkably well in getting behind the myth to an honest and insightful look at the extraordinary man and his extraordinary creation—Apple. Isaacson brings out the three defining characteristics of Job’s approach to everything:

1. Intensity of focus on the goal: Building the best possible product, even sometimes at the apparent expense of caring about the people who are building it
2. Independence of thought: Looking at fundamentals, especially how to build what the user didn’t yet know that they wanted, without concession to prevailing wisdom on engineering, business models or management style
3. Deep commitment to aesthetics: As Jobs described it, the combination of technology and the humanities in every aspect of the product experience—the physical design, the user interface, the retail experience, the access to music and video content, even the way the product was nestled in its box.

Many of us aspire to some of Job’s best qualities (though the world would not necessarily be a better place if we possessed all of his quirks too). His accomplishments do, however, set a challenge to all of us. How do we look at a tough problem—whether it’s building a new electronics platform, addressing rising energy costs or raising a daughter—and come up with a fundamentally better, more complete, more responsible path to success?

I’ve been thinking about this challenge in a new domain—advanced computational imaging and video analysis. This is not just big computational problem. Many of the applications really want trillions of pixel operations per second. But it’s also a challenge in harnessing the creativity of the imaging and video community. Bright imaging algorithm experts come up with clever new methods constantly, but few of those algorithms make it into the mainstream because they are so hard to implement. The extraordinary computation requirements usually make totally hard-wired logic implementations the only deployment option.

But what if we could do trillions of operations per second in a small fraction of a watt, in the corner of a small chip? What if we could change the basic programming model for imaging so that new algorithms (for 3D gesture recognition, for facial expression interpretation, for dramatic image improvement) could just compile and run without specialized software development effort? What if we could map out a scaling to performance levels 100 to 1,000 times the performance of today’s typical mobile device processors? Now that might be worth doing!

I’ve also been looking back at my last post. I was then just getting ready to apply my model of “preparation + intensity = success” to a foot race. Well, it worked! I had perfect running conditions in Sacramento—cold and clear. I went out faster than I planned, but managed to keep the pace (eight minutes per mile) for the whole distance, finishing in 3 hours, 30 minutes, which is 10 minutes ahead of my Boston Marathon 2013 qualifying time. That’s a good run for an old guy like me!

–Chris Rowen is chief technology officer at Tensilica.