An Attempt to Devise a Pairwise Ising-Type Maximum Entropy Model Integrated Cost Function for Optimizing SNN Deployment

The deployment process of a spiking neural network (SNN) can involve partitioning a neural network and mapping partitions onto processing units within the neuromorphic hardware. Searching for optimal deployment schemes presents an NP-hard problem. Optimization of deployment schemes encounters challenges in devising computationally effective cost functions for optimization objectives such as communication time consumption and energy efficiency. These kinds of objectives necessitate consideration of network dynamics shaped by neuron activity patterns, demanding intricate mathematical analyses or simulations for integrating them into a cost model for the deployment of an SNN. The network dynamics are hardware-independent and can be modeled separately from specific hardware configurations. Our approach employs a pairwise Ising-type maximum entropy model, which has shown its effectiveness in accurately reproducing pairwise correlations among components in a system. We utilized this model to capture network dynamics, upon which a cost function is built incorporating hardware-specific parameters. We conducted an extremely preliminary investigation using the SpiNNaker machine. We show that the existing model training can also be computationally complex. Currently, we still lack sufficient evidence to substantiate the effectiveness of our proposed methods. Further efforts is needed to explore integrating network dynamics into SNN deployment.