DNAsim: Evaluation framework for Digital Neuromorphic Applications

Neuromorphic architectures implement low-power machine learning applications using spike-based biological neuron models and bio-inspired algorithms. Prior work on mapping Spiking Neural Networks (SNNs) focused mainly on minimizing inter-core spike communication and on specific computing architectures like crossbar memories. SNN mapping choices on a neuromoprhic multi-processor platform can have varying effects on performance. In this paper we introduce a simulation framework that enables the generation and evaluation of SNN mappings on a user-defined neuromorphic hardware model. Our simulator can evaluate performance of applications based on their spike activities, the hardware model, and the application’s mapping on the hardware, by taking into account inter-core communication as well as the computation load per tile. We create two hardware models based on reported work in literature and show the evaluation of different mapping scenarios for a stateof-the-art SNN benchmark.