NeST: Neuromorphic Stochastic Thermodynamics

Neuromorphic Computing is a field that started three decades ago, inspired by the efficiencies of the information processing performed by biological brains. Its main goal is to replicate the complex architecture and functionality of biological neural circuits, but using in-silico circuits. There are several reasons for pursuing this goal. One is to create an alternative to the currently prevalent VonNeumann architecture, to reduce energetic costs of running computers. 

Stochastic thermodynamics is a recently developed body of work that extends conventional statistical physics to systems that are arbitrarily far from thermal equilibrium, with arbitrarily many degrees of freedom that are all changing on fast timescales. This makes it ideally suited to investigate the energetic behavior alternative computer architectures. Accordingly, in this workshop we will for the first time start to investigate how best to design neuromorphic computers. 

This material is based upon work supported by the U.S. National Science Foundation under award No. 2529902. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.