The Discrete Langevin Machine: Bridging the Gap Between Thermodynamic and Neuromorphic Systems
This work addresses the challenge of bridging thermodynamic and neuromorphic systems for researchers in computational physics and neuromorphic computing, representing a novel method rather than an incremental improvement.
The paper tackles the problem of computing exact results for Boltzmann-distributed systems using neuromorphic hardware by introducing the Langevin machine, a network architecture based on discrete Langevin dynamics, and demonstrates its ability to achieve quantitative exact results with LIF neurons.
A formulation of Langevin dynamics for discrete systems is derived as a class of generic stochastic processes. The dynamics simplify for a two-state system and suggest a network architecture which is implemented by the Langevin machine. The Langevin machine represents a promising approach to compute successfully quantitative exact results of Boltzmann distributed systems by LIF neurons. Besides a detailed introduction of the dynamics, different simplified models of a neuromorphic hardware system are studied with respect to a control of emerging sources of errors.