Thermodynamic Computing Chips Using Langevin Dynamics
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Solution Overview
Problem
Existing algorithms for machine learning, such as those using Bayesian statistics, require complex calculations that increase execution time and energy consumption, leading to inefficiencies in classical computing devices.
Innovation Solution
A self-learning neuro-thermodynamic computing system utilizing multi-chip architectures, where oscillators on thermodynamic chips are mapped to neurons and evolve according to Langevin dynamics, enabling energy-based models to be learned and inferred efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex statistical calculations are performed using classical computing devices, then statistical probabilities can be generated, but execution time and energy consumption increase
Solution Approach 1:
The patent replaces classical mechanical computing systems with a thermodynamic system that uses physical oscillators to naturally compute statistical probabilities. The oscillators evolve according to Langevin dynamics, where their final states directly represent the statistical distribution, eliminating the need for iterative numerical calculations and significantly reducing computation time.
Solution Approach 2:
The patent changes the computational approach from deterministic numerical iteration to stochastic physical evolution. By mapping computational variables to physical oscillator parameters (position, momentum, temperature), the system leverages thermal fluctuations and dissipative dynamics to naturally sample from the desired probability distribution, achieving results in physical time rather than computational time.
2Measurement precision
If complex statistical calculations are performed using classical computing devices, then statistical probabilities can be generated, but energy consumption increases
Solution Approach 1:
The patent replaces energy-intensive classical computing operations with a thermodynamic system that uses minimal energy to maintain oscillator evolution. The system leverages natural thermal fluctuations and dissipative dynamics, where the energy cost is dominated by maintaining the temperature gradient rather than performing computational operations, dramatically reducing overall energy consumption.
Solution Approach 2:
The thermodynamic system performs self-computation through natural physical processes. The oscillators automatically evolve toward their equilibrium distribution governed by the Hamiltonian, without requiring external computational intervention. The system uses its own thermal environment to drive the sampling process, eliminating the need for active computational energy expenditure.
3Productivity
If oscillators evolve according to Langevin dynamics, then energy-based models can be learned efficiently, but the system complexity increases
Solution Approach 1:
The patent divides the thermodynamic computing system into distinct functional modules: oscillators representing neurons, coupling elements representing weights, and separate chips for different computational tasks (e.g., visible units, hidden units, weight updates). This modular segmentation allows each component to be optimized independently while maintaining overall system efficiency and manageability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for faster and more energy-efficient implementation of machine learning algorithms by directly sampling neurons, reducing the need for complex statistical calculations and improving computational efficiency.
Implementation Method 1
oscillators on thermodynamic chips are mapped to neurons and evolve according to Langevin dynamics
Data Source
AI summary
A self-learning neuro thermodynamic computing device comprising thermodynamic computing chips as well as systems and methods for performing computing using a self-learning neuro thermodynamic computing device are disclosed. In some embodiments, the self-learning neuro thermodynamic computing device may automatically learn weights and biases to be used for inference generation using Langevin dynamics. In some embodiments, the self-learning neuro thermodynamic computing device comprises two or more coupled thermodynamic chips, such as a clamped thermodynamic chip configured to be clamped to input data (e.g. training data or test data), an un-clamped thermodynamic chip, and a server thermodynamic chip that coordinates between the clamped and un-clamped thermodynamic chips such that weight and bias values are maintained approximately the same between the clamped and un-clamped thermodynamic chips.