Neuromorphic computers are modeled after the structure of the human brain, and researchers are finding that they can tackle difficult mathematical problems at the heart of many scientific and engineering fields.
In a study published in Nature Machine Intelligence, Sandia National Laboratories computational neuroscientists Brad Theilman and Brad Aimone introduce a new algorithm that allows neuromorphic hardware to solve partial differential equations, or PDEs. These equations form the mathematical basis for describing systems such as fluid flow, electromagnetic behavior, and the strength of physical structures.
The results show that neuromorphic systems can not only solve these equations, but can do so with impressive efficiency. According to the researchers, this advance could open the door to the world’s first neuromorphic supercomputer, with major implications for energy-efficient computing in national security and other demanding applications.
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