Application - Hardware Co-Optimization of Crossbar-Based Neuromorphic Systems

Spiking Neural Networks (SNNs) executed on neuro-morphic hardware (NmC) have shown great potential to perform a class of learning and inference tasks with low latency and high energy efficiency. However, there are an ever increasing number of design hyperparameters related to the learning algorithms, neuron and synaptic models, neural network architectures and neuromorphic hardware that are required to design the SNN model. These hyperparameters determine the accuracy and en-ergy efficiency of the trained model and often require application, hardware, and software expertise, and are often determined by trial and error, which is a difficult, ad-hoc, and time consuming process. Therefore, there is a need for an automated hyperpa-rameter tuning framework that can search a set of software and hardware hyperparameters to find an SNN model with optimal application accuracy and energy efficiency, when inferred on a NmC. To this end, we propose a framework to co-optimize the accuracy and energy consumption of an SNN model executed on an cross-bar based NmC. The proposed framework integrates three key components: (1) SNNTorch, to train and validate SNN model, (2) SNN-Neurosim, a custom energy estimator for cross-bar based NmC and (3) DeepHyper, a scalable hyperparameter tuning approach to explore the hyperparameter search space of the SNN model and neuromorphic hardware. We evaluate the framework using SNN models trained for scientific applications to (1) parameterize the planetary boundary layer (PBL) using data from the Weather Research Forecast (WRF) model, (2) detect Bragg diffraction peaks for use in X-ray based diffraction microscopy and (3) the reconstruction of high-fidelity amplitude and phase for ptychographic imaging.