Thermodynamic Transformer Computing With Oscillators to Reduce Latency
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Solution Overview
Problem
Machine learning algorithms using classical computing devices face challenges with increased execution time and energy consumption due to complex statistical probability calculations, leading to inefficiencies in latency and energy usage.
Innovation Solution
Implementing transformer neural networks on thermodynamic chips using oscillators to perform computations, leveraging thermodynamic processes such as Langevin dynamics and engineered potentials to accelerate operations like matrix multiplication, attention layers, and activation functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical computing devices are used to perform statistical probability calculations, then calculation accuracy is maintained, but execution time and energy consumption increase significantly
Solution Approach 1:
The patent replaces classical digital computing systems with a thermodynamic computing system that uses physical oscillators to perform calculations. The system maps computational operations onto thermodynamic processes, where oscillators naturally evolve according to engineered potentials to produce statistical probabilities. This substitution of mechanical/digital computation with thermodynamic processes fundamentally changes how calculations are performed, enabling parallel processing of probabilistic computations without the sequential bottlenecks of classical devices.
Solution Approach 2:
The patent changes the fundamental parameters of computation by transitioning from discrete digital states to continuous thermodynamic states. The system uses oscillator frequencies, amplitudes, and phase relationships as computational parameters instead of binary bits. By adjusting thermodynamic parameters such as temperature, coupling strengths, and potential energies, the system can dynamically control computational behavior and achieve accurate statistical probability generation with significantly reduced execution time.
2Reliability
If classical computing devices are used to perform statistical probability calculations, then computational correctness is ensured, but energy consumption increases
Solution Approach 1:
The patent replaces energy-intensive digital logic operations with thermodynamic processes that naturally evolve toward equilibrium states. Instead of using powered transistors and memory cells to compute probabilities, the system uses oscillators that naturally sample from Boltzmann distributions defined by engineered potentials. This substitution eliminates the need for continuous energy input to maintain computational states, as the thermodynamic system passively evolves according to physical laws, dramatically reducing energy consumption while maintaining computational correctness.
Solution Approach 2:
The thermodynamic computing system is self-regulating and self-organizing, requiring minimal external control. The oscillators automatically evolve toward their equilibrium distributions based on the engineered potentials, without needing active control circuits or refresh operations. The system uses its own thermodynamic fluctuations and natural dynamics to perform computations, eliminating the need for external energy input during the computation phase and achieving reliable probabilistic outputs through self-organized criticality.
3Adaptability or versatility
If transformer neural networks are implemented on classical devices, then algorithmic functionality is achieved, but latency increases
Solution Approach 1:
The patent segments the transformer neural network into distinct thermodynamic modules, each implementing specific operations such as attention mechanisms, feedforward layers, and activation functions. Each module consists of oscillators configured with appropriate potentials to perform its designated function. This segmentation allows independent optimization and parallel execution of different network layers and operations, eliminating the sequential processing bottlenecks inherent in classical implementations and significantly reducing overall latency.
Solution Approach 2:
The thermodynamic implementation enables continuous computation without the discrete clock-cycle constraints of digital systems. Oscillators continuously evolve according to their potentials, allowing gradient flow and probability sampling to occur in real-time rather than in discrete steps. This continuity eliminates idle periods between computational operations and allows the system to process information as it flows through the network, dramatically reducing latency while maintaining full transformer functionality.
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 significantly reduces execution time and energy consumption by harnessing thermodynamic processes, enabling faster inference and training times for machine learning tasks.
Implementation Method 1
leveraging thermodynamic processes such as Langevin dynamics and engineered potentials to accelerate operations
Data Source
AI summary
Systems, methods and computer readable media relating to neuro-thermodynamic computers configured to implement one or more components of a transformer neural network architecture, wherein the transformer neural network architecture is configured to perform operations of a transformer neural network. Thermodynamic data may be used as input to one or more thermodynamic chips comprising oscillators, wherein thermodynamic evolution according to one or more energy potentials governing the oscillators enable results of a transformer neural network architecture, or at least intermediate results, to be obtained by respective ones of the oscillators. Furthermore, the results, encoded as thermodynamic data in position degree of freedoms of respective oscillators, of one component may be used as input to another component.