The Discrete Langevin Machine: Bridging the Gap Between Thermodynamic and Neuromorphic Systems
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Partitioned integrators for thermodynamic parameterization of neural networks
Traditionally, neural networks are parameterized using optimization procedures such as stochastic gradient descent, RMSProp and ADAM. These procedures tend to drive the parameters of the network toward a local minimum. I…
Thermodynamic Bayesian Inference
A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks including model selection. However, the…
Bayesian InferenceModel SelectionUncertainty QuantificationConsistent Projection of Langevin Dynamics: Preserving Thermodynamics and Kinetics in Coarse-Grained Models
Coarse graining (CG) is an important task for efficient modeling and simulation of complex multi-scale systems, such as the conformational dynamics of biomolecules. This work presents a projection-based coarse-graining f…
A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
To address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack that leverages stochastic analog processes in ph…
Learning Stochastic Thermodynamics Directly from Correlation and Trajectory-Fluctuation Currents
Markedly increased computational power and data acquisition have led to growing interest in data-driven inverse dynamics problems. These seek to answer a fundamental question: What can we learn from time series measureme…