Computers designed to mimic the structure of the human brain are showing an unexpected strength. They can solve some of the demanding mathematical equations that lie at the heart of major scientific and engineering problems.
In a study published in Nature Machine Intelligence, Sandia National Laboratories computational neuroscientists Brad Theilman and Brad Aimone introduced a new algorithm that allows neuromorphic hardware to solve partial differential equations, or PDEs -- the mathematical foundation for modeling phenomena such as fluid dynamics, electromagnetic fields and structural mechanics.
The results demonstrate that neuromorphic systems can handle these equations efficiently. The advance could help open the door to the first neuromorphic supercomputer, offering a new path toward energy efficient computing for national security and other critical applications.
The research was funded by the Department of Energy's Office of Science through the Advanced Scientific Computing Research and Basic Energy Sciences programs, as well as the National Nuclear Security Administration's Advanced Simulation and Computing program.
Solving Partial Differential Equations With Brain Like Hardware
Partial differential equations are essential for simulating real world systems. They are used to forecast weather, analyze how materials respond to stress, and model complex physical processes. Traditionally, solving PDEs requires enormous computing power. Neuromorphic computers approach the problem differently by processing information in ways that resemble how the brain operates.
"We're just starting to have computational systems that can exhibit intelligent-like behavior. But they look nothing like the brain, and the amount of resources that they require is ridiculous, frankly," Theilman said.
For years, neuromorphic systems were mainly viewed as tools for pattern recognition or for speeding up artificial neural networks. Few expected them to manage mathematically rigorous problems such as PDEs, which are typically handled by large scale supercomputers.
Aimone and Theilman were not surprised by the outcome. They argue that the human brain routinely carries out highly complex calculations, even if people are unaware of it.
"Pick any sort of motor control task -- like hitting a tennis ball or swinging a bat at a baseball," Aimone said. "These are very sophisticated computations. They are exascale-level problems that our brains are capable of doing very cheaply."
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