An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC

--This work presents a multi-layer DNN scheduling framework as an extension of OctopuScheduler, providing an end-to-end flow from PyT orch models to inference on a single SpiN-Naker2 chip. T ogether with a front-end comprised of quantization and lowering steps, the proposed framework enables the edge-based execution of large and complex DNNs up to transformer scale using the neuromorphic platform SpiNNaker2. The efficient deployment of Deep Neural Networks (DNNs) on constrained devices has the potential to revolutionize the entire edge industry. While the primary energy challenges are associated with datacenter workloads [1], mapping DNN models efficiently to the edge enables the development of smarter infrastructure nodes. Neuromorphic computing stands out as a particularly promising approach to significantly reduce the energy footprint of these AI workloads by emulating the extreme efficiencies of biological brains [2].