Hierarchical Network Partitioning for Reconfigurable Large-Scale Neuromorphic Systems

We present an efficient and scalable partitioning method for mapping large-scale neural network models to reconfigurable neuromorphic hardware. The partitioning framework is optimized for compute-balanced, memory -efficient parallel processing targeting low-latency execution and dense synaptic storage, with minimal routing across various compute cores. We demonstrate highly scalable and efficient ...