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 physical hardware. In this work, we focus on energy-based thermodynamic computing where the stochastic process is well described by Langevin dynamics with tunable energy potentials. The implementation of such potentials in physical hardware enables us to generate and sample from basic parameterized energy-based models. We demonstrate how to construct and train popular classes of
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- PossiblePossibly related (embedding) · 46%Researchers Propose Thermodynamic Computing Architecture That Could Dramatically Reduce AI Energy Use - The Quantum Insider →
- LinkedLinked via arxiv author · 85%Owen Lockwood →
“A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing”
- LinkedLinked via arxiv author · 85%Jérémy Béjanin →
“A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing”
- LinkedLinked via arxiv author · 85%Joost Bus →
“A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing”
- LinkedLinked via arxiv author · 85%Christopher Chamberland →
“A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing”
- LinkedLinked via arxiv author · 85%Patrick Huembeli →
“A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing”
- LinkedLinked via arxiv author · 85%Frank Schäfer →
“A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing”
- LinkedLinked via arxiv author · 85%Guillaume Verdon →
“A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing”