Kenny Walter | July 9, 2017
A new computer system aims to solve some of the computing world’s hardest challenges in record time.
Researchers from the Georgia Institute of Technology and the University of Notre Dame have taken cues from the human brain in creating a new system that employs a network of electronic oscillators to solve graph coloring tasks in a fraction of the time.
“We wanted to find a way to solve a problem without using the normal binary representations that have been the backbone of computing for decades,” Arijit Raychowdhury, an associate professor in Georgia Tech's School of Electrical and Computer Engineering, said in a statement. “Applications today are demanding faster and faster computers to help solve challenges like resource allocation, machine learning and protein structure analysis—problems which at their core are closely related to graph coloring.
“But for the most part, we've reached the limitations of modern digital computer processors,” he added. “Some of these problems are so computationally difficult to perform, it could take a computer several weeks to solve.”
A graph coloring system begins with a graph—a visual representation of a set of objects connected in some way and the computer solves the problem by assigning each object a color. However, two objects directly connected cannot share the same color, the aim being to color all objects in the graph using the smallest number of different colors.
The researchers created a system similar to the human brain, where processing is handled collectively, such as a neural oscillatory network, rather than with a central processor.
“It's the notion that there is tremendous power in collective computing,” Suman Datta, Chang Family professor in Notre Dame's College of Engineering and one of the study's co-authors, said in a statement. “In natural forms of computing, dynamical systems with complex interdependencies evolve rapidly and solve complex sets of equations in a massively parallel fashion.”