Your math teacher lied to you. Sometimes getting your sums wrong is a good thing.
So says Joseph Bates, cofounder and CEO of Singular Computing, a company whose computer chips are hardwired to be incapable of performing mathematical calculations correctly. Ask it to add 1 and 1 and you will get answers like 2.01 or 1.98.
The Pentagon research agency DARPA funded the creation of Singular’s chip because that fuzziness can be an asset when it comes to some of the hardest problems for computers, such as making sense of video or other messy real-world data. “Just because the hardware is sucky doesn’t mean the software’s result has to be,” says Bates.
A chip that can’t guarantee that every calculation is perfect can still get good results on many problems but needs fewer circuits and burns less energy, he says.
Bates has worked with Sandia National Lab, Carnegie Mellon University, the Office of Naval Research, and MIT on tests that used simulations to show how the S1 chip’s inexact operations might make certain tricky computing tasks more efficient. Problems with data that comes with built-in noise from the real world, or where some approximation is needed, are the best fits. Bates reports promising results for applications such as high-resolution radar imaging, extracting 3-D information from stereo photos, and deep learning, a technique that has delivered a recent burst of progress in artificial intelligence.
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