With the increasing development of neuromorphic platforms and their related software tools as well as the increasing scale of spiking neural network (SNN) models, there is a pressure for interoperable and scalable representations of network state. In response to this, we discuss a parallel extension of a widely used format for efficiently representing sparse matrices, the compressed sparse row (CSR), in the context of supporting the simulation and serialization of large-scale SNNs. Sparse matrices for graph adjacency structure provide a natural fit for describing the connectivity of an SNN, and prior work in the area of parallel graph partitioning has developed the distributed CSR (dCSR) format for storing and ingesting large graphs. We contend that organizing additional network information, such as neuron and synapse state, in alignment with its adjacency as dCSR provides a straightforward partition-based distribution of network state. For large-scale simulations, this means each parallel process is only responsible for its own partition of state, which becomes especially useful when the size of an SNN exceeds the memory resources of a single compute node. For potentially long-running simulations, this also enables network serialization to and from disk (e.g. for checkpoint/restart fault-tolerant computing) to be performed largely independently between parallel processes. We also provide a potential implementation, and put it forward for adoption within the neural computing community.

翻译：随着神经形态平台及其相关软件工具的不断发展，以及脉冲神经网络（SNN）模型规模的日益扩大，对网络状态的可互操作与可扩展表示的需求与日俱增。为此，我们讨论了一种广泛应用于高效稀疏矩阵表示的压缩稀疏行（CSR）格式的并行扩展，以支持大规模SNN的模拟与序列化。图邻接结构的稀疏矩阵天然适合描述SNN的连接性，而并行图划分领域的先前工作已开发出用于存储和读取大规模图的分布式CSR（dCSR）格式。我们认为，将额外的网络信息（如神经元和突触状态）与其邻接结构按dCSR方式组织，可实现基于分区的网络状态直接分布。对于大规模模拟而言，这意味着每个并行进程仅负责其自身的状态分区，当SNN规模超过单个计算节点的内存资源时，这一特性尤为实用。对于可能长时间运行的模拟，该方法还允许并行进程之间基本独立地执行网络向磁盘的序列化与反序列化（例如用于检查点/重启容错计算）。我们同时提供了一种潜在实现方案，并提议在神经计算社区中推广应用。