Solving Constraint Satisfaction Problems Using the Loihi Spiking Neuromorphic Processor

In many cases, low power autonomous systems need to make decisions extremely efficiently. However, as a potential solution space becomes more complex, finding a solution quickly becomes nearly impossible using traditional computing methods. Thus, in this work we present a constraint satisfaction algorithm based on the principles of spiking neural networks. To demonstrate the validity of this algorithm, we have shown successful execution of the Boolean satisfiability problem (SAT) on the Intel Loihi spiking neuromorphic research processor. Power consumption in this spiking processor is due primarily to the propagation of spikes, which are the key drivers of data movement and processing. Thus, this system is inherently efficient for many types of problems. However, algorithms must be redesigned in a spiking neural network format to achieve the greatest efficiency gains. To the best of our knowledge, the work in this paper exhibits the first implementation of constraint satisfaction on a low power embedded neuromorphic processor. With this result, we aim to show that embedded spiking neuromorphic hardware is capable of executing general problem solving algorithms with great areal and computational efficiency.