No one expects rotary phones or adding machines to come crawling back from oblivion. Or so we thought...
There are diminishing returns on the digital model, he said, yet it still dominates the industry. “If we applied as many people and as much money to the analog domain, I think we could have some kind of analog coprocessing happening to accelerate the existing algorithms. Digital computers are very good at scalability. Analog is very good at complex interactions between variables. In the future, we may combine these advantages.”fully functional, but it had a problem: It was not easy to use.
When I spoke with Achour, she was entertaining and engaging, delivering terminology at a manic pace. She told me she had originally intended to be a doctor but switched to computer science after having pursued programming as a hobby since middle school. “I had specialized in math modeling of biological systems,” she said. “We did macroscopic modeling of gene protein hormonal dynamics.
I liked the idea of a former medical student alleviating the pain of chip designers who had difficulty using their own hardware. But what was her take on applications? Are there any? “Yes, whenever you’re sensing the environment,” she said. “And reconfigurability lets you reuse the same piece of hardware for multiple computations. So I don’t think this is going to be relegated to a niche model. Analog computation makes a lot of sense when you’re interfacing with something that is inherently analog.” Like the real world, with all its muzziness.
But what if you don’t ignore air resistance? The faster the ball falls, the more air resistance it encounters. But gravity remains constant, so the ball’s speed doesn’t increase at a steady rate but tails off until it reaches terminal velocity. You can express this in a differential equation too, but it adds another layer of complexity. I won’t get into the mathematical notation , because the take-home message is all that matters.
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