Networks underlie such real-life problems as determining the most efficient route for a trucking company to deliver lifesaving drugs and calculating the smallest number of mutations required to transform one string of DNA into another. Instead of relying on software to tackle these computationally intensive puzzles, researchers created a design for an electronic hardware system that directly replicates the architecture of many types of networks.
The researchers demonstrated the hardware system using a computational technique known as race logic to solve a variety of complex puzzles both rapidly and with a minimum expenditure of energy. Race logic requires less power and solves network problems more rapidly than general-purpose computers.
A key feature of race logic is that it encodes information differently from a standard computer. Digital information is typically encoded and processed using values of computer bits — a 1 if a logic statement is true and a 0 if it’s false. When a bit flips its value, say from 0 to 1, it means that a particular logic operation has been performed to solve a mathematical problem.
In contrast, race logic encodes and processes information by representing it as time signals — the time at which a particular group of computer bits transitions, or flips, from 0 to 1. Large numbers of bit flips are the primary cause of the large power consumption in standard computers. In this respect, race logic offers an advantage because signals encoded in time involve only a few carefully orchestrated bit flips to process information, requiring much less power than signals encoded as 0s or 1s.
Computation is then performed by delaying some time signals relative to others, determined by the physics of the system under study. For example, consider a group of truck drivers who start at point A and must deliver medicine to point E as fast as possible. Different possible routes go through three intersections: B, C, and D. To determine the most efficient route, the race logic circuit evaluates each possible segment of the trip such as A-B and AD. If A-B takes more time to travel than AD, whether it’s because the path is longer or has more traffic, A-B will be assigned a longer delay time. In the team’s design, the longer time delay is implemented by adding additional resistance to the slower segment.
Race logic does involve a race but in this contest, all the truck drivers initially drive in different directions. To determine which route to the final destination is fastest, they race over all possible routes through the different intermediate delivery points. In the new circuit, the researchers inserted a group of time-encoded signals at the starting point, each acting as a different driver that speeds through the team’s simulated hardware circuit.
Whenever a driver arrives at an intermediate destination point in the race, the model system sends out new drivers (new time signals) who fan out in different directions to the remaining destinations. If a driver arrives at a destination that another driver has already been to, that driver drops out because the path is no longer competitive. The winner of the race — the first driver to arrive at the end of the circuit — indicates the solution to the particular puzzle that the hardware was programmed to solve.
The design, which has not yet been incorporated into a working device, can handle a much broader class of networks, enabling race logic to tackle a wider variety of computational puzzles. These puzzles include finding the best alignment between two proteins or two strings of nucleotides — the molecules that form the building blocks of DNA — and determining the shortest path between two destinations in a network.