| Summary: | While task mapping for multi-core systems is known as an NP-hard problem, mapping for neuromorphic systems even scale it up due to a high number of neurons per core and a high number of core per system. Moreover, mapping for neuromorphic systems also has several challenges such as heterogeneous computing core or communication fabrics, and potential defects in neurons or routing units. Therefore, this paper presents a genetic algorithm framework named HeterGenMap which is a Genetic Algorithm framework for mapping multiple-layer Spiking Neural Network systems to solve the aforementioned problems. The results show that HeterGenMap improves the overall communication cost by 11.04-26.77% in comparison to the linear mapping. Moreover, under link faulty scenarios, neuron defects, or multi-chip designs, HeterGenMap can reduce the communication cost by 3.41-31.34%, 7.01%-41.51%, and 34.21-45.56% in comparison to the linear approach, respectively. The validation in hardware also demonstrated that HeterGenMap reduces the inference time by 63.10-77.87% from the linear mapping.
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