Stochastic Learning Computing Inputs for Low Power AI
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Solution Overview
Problem
Current machine learning and artificial intelligence technologies face limitations in addressing large-scale problems due to high computational complexity and power consumption, making them unsustainable with existing infrastructure.
Innovation Solution
The implementation of stochastic learning methods that utilize stochastic representations and computations, integrating principles from the Schrödinger equation to reduce computational requirements and power consumption, allowing for efficient processing of data without the need for preprocessing or feature engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning and artificial intelligence methods are used to address large-scale problems, then computational accuracy can be maintained, but computational complexity and power consumption increase exponentially
Solution Approach 1:
The patent replaces traditional deterministic mechanical computing systems with a stochastic computing system that uses probabilistic methods and random processes. This substitution fundamentally changes the computational approach from exact deterministic calculations to statistical estimation, dramatically reducing power consumption while maintaining acceptable accuracy for large-scale problems
Solution Approach 2:
The patent changes the fundamental parameters of computation by introducing stochastic elements and probabilistic representations. Instead of using fixed deterministic values and operations, the system employs random variables, probability distributions, and statistical methods, transforming the computational paradigm to achieve lower energy consumption
2Productivity
If traditional AI systems are implemented at large scale, then problem-solving capability is enhanced, but infrastructure sustainability deteriorates due to power and cooling requirements
Solution Approach 1:
The patent replaces energy-intensive deterministic computing infrastructure with a stochastic computing system that inherently requires less power. This substitution enables large-scale problem-solving capability while significantly reducing the harmful environmental factors associated with power consumption and heat dissipation
Solution Approach 2:
The stochastic computing system leverages natural random processes and probabilistic phenomena that occur without continuous external energy input. By utilizing inherent stochasticity in physical systems and mathematical randomness, the system reduces its dependency on sustained high-power infrastructure
3Measurement precision
If deterministic computing methods are used, then precise calculations can be performed, but computational complexity increases for large-scale problems
Solution Approach 1:
The patent replaces complex deterministic calculation systems with simpler stochastic computing mechanisms. By using probabilistic methods and random processes, the system achieves comparable calculation precision for large-scale problems while dramatically reducing computational complexity and the sophistication of required computational infrastructure
Data Source
AI summary
A method includes accessing, using a computing system, data including a plurality of variables, each variable having one or more elements. The method includes determining stochastic partial differences between elements of respective variables of the plurality of variables and combining respective stochastic partial differences into groups including one or more stochastic partial difference equations (SPDEs). The method includes evaluating, using a fitness measure criterion, the one or more SPDEs in relation to an objective function. The method includes determining, by the computing system, based on the evaluating, a prediction related to at least one data input to an application executable by one of the computing system or a second computing system communicatively coupled to the computing system. The at least one data input relies, at least in part, on one or more of the plurality of variables.