General Purpose Computation with Spiking Neural Networks - Programming, Design Principles, and Patterns.

Neuromorphic computer architectures utilize an event-based paradigm to operate in low-power environments and achieve high-throughput with massive parallelism. The integrate-and-fire (IF) neuron model has become a greatest common denominator among many emerging architectures built from materials with neuronlike response properties. There is recent interest in using these architectures as general purpose computing devices, especially for problems that are computationally demanding such as constraint satisfaction, integer factorization, and numerical analysis. However, programming a spiking neural network (SNN) to perform these kinds of tasks remains a difficult problem. There is currently a lack of overarching principles to guide this kind of development, and specific solutions generally remain unportable between different architectures. We identify some of the challenges to using neuromorphic computers for general purpose computing. To address these challenges, we introduce four design principles that aim to facilitate good design on a SNN architecture. We then describe several patterns that solve recurring neuromorphic design challenges, including serial execution on a parallel architecture and the implementation of structured memory in a SNN.