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

• URL: http://arxiv.org/abs/2407.07014v2
• Date: Thu, 11 Jul 2024 00:15:32 GMT
• Title: An Attempt to Devise a Pairwise Ising-Type Maximum Entropy Model Integrated Cost Function for Optimizing SNN Deployment
• Authors: Wanhong Huang,
• Abstract summary: A spiking neural network (SNN) deployment process often involves partitioning the neural network onto processing units within the neuromorphic hardware. Finding optimal deployment schemes is an NP-hard problem. These objectives require consideration of network dynamics shaped by neuron activity patterns. Our approach focuses on network dynamics, which are hardware-independent and can be modeled separately from specific hardware configurations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The deployment process of a spiking neural network (SNN) often involves partitioning the neural network and mapping these partitions onto processing units within the neuromorphic hardware. Finding optimal deployment schemes is an NP-hard problem. Optimizing these schemes presents challenges, particular in devising computationally effective cost functions optimization objectives such as communication time consumption and energy efficiency. These objectives require consideration of network dynamics shaped by neuron activity patterns, demanding intricate mathematical analyses or simulations for integrating them into a cost model for SNN development. Our approach focuses on network dynamics, which are hardware-independent and can be modeled separately from specific hardware configurations. We employ a pairwise Ising-type maximum entropy model, which is a model show effective in accurately capturing pairwise correlations among system components in a collaborative system. On top of this model, we incorporates hardware and network structure-specific factors to devise a cost function. We conducted an extremely preliminary investigation using the SpiNNaker machine. We show that the ising model training can also be computationally complex. Currently, we lack sufficient evidence to substantiate the effectiveness of our proposed methods. Further efforts is needed to explore integrating network dynamics into SNN deployment.

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