Spike-based primitives for graph algorithms
This work addresses graph algorithm execution for neuromorphic computing, but it appears incremental as it adapts existing spiking neuron concepts to a new application.
The paper tackled the problem of implementing graph algorithms on neuromorphic hardware by using spiking neuron dynamics for low-level graph operations, with results including hardware-agnostic routines that work with static or plastic synapses.
In this paper we consider graph algorithms and graphical analysis as a new application for neuromorphic computing platforms. We demonstrate how the nonlinear dynamics of spiking neurons can be used to implement low-level graph operations. Our results are hardware agnostic, and we present multiple versions of routines that can utilize static synapses or require synapse plasticity.