Spike-Based Primitives for Graph Algorithms

📝 Original Paper Info

- Title: Spike-based primitives for graph algorithms
- ArXiv ID: 1903.10574
- Date: 2019-03-27
- Authors: Kathleen E. Hamilton and Tiffany M. Mintz and Catherine D. Schuman

📝 Abstract

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.

💡 Summary & Analysis

This paper explores the potential of neuromorphic computing platforms for implementing graph algorithms and graphical analysis, utilizing the nonlinear dynamics of spiking neurons. The authors demonstrate how these neurons can be used to perform low-level graph operations. Their approach is hardware-agnostic, allowing flexibility in implementation across different types of neuromorphic systems.

The primary focus is on addressing the computational challenges faced by traditional CPU or GPU-based platforms when dealing with complex network structures and large datasets for graph algorithms and analysis. The authors propose using spike-based primitives, which mimic how neurons communicate in the brain through spikes, to implement these operations more efficiently.

Two versions of routines are presented: one utilizing static synapses and another requiring synaptic plasticity. This flexibility allows for adaptation to various hardware configurations while maintaining performance. The results highlight the feasibility and potential benefits of this approach in enhancing graph processing capabilities on neuromorphic platforms.

This research is significant as it opens up new avenues for improving the efficiency and scalability of graph algorithms, especially for handling complex networks and large datasets. It contributes to advancing the field by demonstrating how biologically-inspired computing techniques can be applied to solve real-world computational problems.

📄 Full Paper Content (ArXiv Source)

📊 논문 시각자료 (Figures)

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