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Probabilistic topic models are popular unsupervised learning methods,including probabilistic latent semantic indexing (pLSI) and latent Dirichletallocation (LDA). By now, their training is implemented on general purposecomputers (GPCs), which are flexible in programming but energy-consuming.Towards low-energy implementations, this paper investigates their training onan emerging hardware technology called the neuromorphic multi-chip systems(NMSs). NMSs are very effective for a family of algorithms called spikingneural networks (SNNs). We present three SNNs to train topic models. The firstSNN is a batch algorithm combining the conventional collapsed Gibbs sampling(CGS) algorithm and an inference SNN to train LDA. The other two SNNs areonline algorithms targeting at both energy- and storage-limited environments.The two online algorithms are equivalent with training LDA by usingmaximum-a-posterior estimation and maximizing the semi-collapsed likelihood,respectively. They use novel, tailored ordinary differential equations forstochastic optimization. We simulate the new algorithms and show that they arecomparable with the GPC algorithms, while being suitable for NMSimplementation. We also propose an extension to train pLSI and a method toprune the network to obey the limited fan-in of some NMSs.

Towards Training Probabilistic Topic Models on Neuromorphic Multi-chip Systems