A Digital Neuromorphic Architecture for Unsupervised Shortest Path Computation on Real-World Graphs

Graphs are popular tools for analyzing interconnected data entities. We propose SENTIENCE, a novel approach to computing the shortest path in a graph in an unsupervised manner drawing inspiration from the hippocampus. SENTIENCE uses laterally-connected neurons to represent nodes and synapses to represent edges. The strength of a synaptic connection is encoded as the axonal delay. When a neuron (source) is excited, a wavefront of neural activity is created that propagates through the graph via the connected nodes. SENTIENCE uses the Eligibility Propagation (E-Prop) algorithm to learn the sequence of wave movement through the shortest path in a graph, which can be obtained by identifying earliest firing (eligible) neighbors by tracing back in time from when the wavefront reaches a destination. We introduce a lightweight hardware design for the axonal plasticity and the E-Prop learning mechanism of SENTIENCE. We propose a tile-based architecture to address the scalability of SENTIENCE for real-world graphs with irregular data dependency. We evaluate SENTIENCE on a Versal VPK 180 FPGA and show that SENTIENCE consumes on average 10× less resources and 12× less power compared to a state-of-the-art.