Probabilistic computing with percolating nanoparticle networks using experimental data with signatures of criticality

Abstract

Percolating networks of nanoparticles (PNNs) are promising systems for neuromorphic computing due to their brain-like network structure and dynamics. In particular, electrical spiking in PNNs meets criteria for criticality, which is thought to be the operating point for biological brains and associated with optimal computation. Previous work showed through simulations that spiking PNNs can be used as the core stochastic component in a probabilistic computing scheme. Here, we demonstrate a route to experimental implementation of an integer factorization algorithm. We outline an important modification to the algorithm previously used and demonstrate factorization of up to six-digit integers. Finally, we explore the effect of criticality in the context of the integer factorization task by comparing critical and non-critical systems. We show significant differences in the probability distribution of states generated by the critical and non-critical systems, though for the task considered, critical systems provide no advantage.

Keywords

• probabilistic computing
• probabilistic bits
• optimization
• criticality
• nanoparticle networks
• neuromorphic computing