AI 中文总结
该研究对比了两款优化小世界通信结构的神经形态多核系统NeoCorAl与MOSAIC，分析其路由特性权衡及通信局部性对路由效率的影响，还探讨了感知路由训练的优化作用。
AI 中文摘要
当神经形态系统扩展到超过单个核心时，核心间的事件通信会成为内存占用、延迟和能耗的主要贡献因素。生物神经系统通过小世界结构应对类似的扩展挑战，将密集的局部连接与稀疏的长程投射相结合。本研究对比了两款采用相同22nm FDSOI工艺实现、专门针对此类连接优化的神经形态多核系统：第一款NeoCorAl采用异步分组交换树结构，结合分层组播；第二款MOSAIC采用基于RRAM的电路交换二维网状结构，在内存中执行路由。我们考察了两款系统在路由灵活性、跳数、内存需求、组播效率和可扩展性方面的权衡，进一步研究基于树和网状的路由的相对效率如何依赖于空间嵌入网络、随机网络和分层网络中的通信局部性，最后讨论了感知路由的训练作为联合优化神经连接、任务性能和硬件可映射性的手段。
英文摘要
As neuromorphic systems scale beyond a single core, inter-core event communication can become a dominant contributor to memory footprint, latency, and energy consumption. Biological neural systems address a similar scaling challenge through small-world organization, combining dense local connectivity with sparse long-range projections. In this work, we compare two recent multicore neuromorphic systems implemented in the same 22-nm FDSOI technology and explicitly optimized for such connectivity. The first, NeoCorAl, uses an asynchronous packet-switched tree with hierarchical multicast, whereas the second, MOSAIC, employs an RRAM-based, circuit-switched two-dimensional mesh that performs routing in memory. We examine the resulting trade-offs in routing flexibility, hop count, memory requirements, multicast efficiency, and scalability. We further study how the relative efficiency of tree- and mesh-based routing depends on communication locality in spatially-embedded, random, and layered networks. Finally, we discuss routing-aware training as a means of jointly optimizing neural connectivity, task performance, and hardware mappability.
