Boolean-Neural Hybrid Circuits for Reverse Hard-Problem Solving
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Solution Overview
Problem
Current methods for solving complex problems like factorization, subset sum, maximum satisfiability, and bitcoin mining are inefficient and require large resources, with existing technologies such as quantum computing, probabilistic spin logic, and memcomputing facing challenges in practical implementation.
Innovation Solution
The development of Boolean-neural hybrid computing circuits that combine traditional two- or three-state logic gates with semi-stochastic neurons, allowing for stochastic search and deterministic storage of solutions, and operate in reverse by applying input data to output pins and reading results from inputs, utilizing invertible logic gates and feedback circuitry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If quantum computing algorithms are used to solve hard problems like factorization, then solution speed is improved exponentially, but practical implementation is limited by small number of qubits available
Solution Approach 1:
The patent replaces quantum mechanical systems with a hybrid Boolean-neural computing system that uses classical logic gates combined with semi-stochastic neurons. This substitution allows solving hard problems like factorization without requiring quantum hardware, thereby avoiding the limitation of small qubit counts while maintaining improved solution speed through the semi-stochastic search mechanism.
2Productivity
If probabilistic spin logic with p-bits is used for factorization, then solution can be extracted from histograms of time fluctuations, but large number of p-bits and long sampling times are required
Solution Approach 1:
The patent inverts the traditional approach by using invertible logic gates that operate in reverse mode. Instead of applying inputs and reading outputs conventionally, the system applies input data to output pins and reads results from input pins. This inversion, combined with semi-stochastic neurons, enables faster convergence to solutions without requiring long sampling times or large numbers of computational units.
Solution Approach 2:
The patent introduces dynamic behavior through semi-stochastic neurons that can transition between deterministic and stochastic modes. This dynamic capability allows the system to adapt its search strategy during computation, achieving faster solutions without requiring excessive sampling time or large numbers of p-bits, thus resolving the contradiction between productivity and time loss.
3Extent of automation
If memcomputing with self-organizing logic gates is used, then problem solutions are found through self-organization, but the internal structure becomes quite complex with multiple memristors and voltage-controlled sources
Solution Approach 1:
The patent merges the advantages of Boolean logic and neural computing into a hybrid system. By combining traditional logic gates with semi-stochastic neurons, the system achieves self-organizing capability for solving hard problems while avoiding the complex internal structure of pure memcomputing implementations that require multiple memristors and voltage-controlled sources per gate.
Solution Approach 2:
The patent changes the operational parameters of logic gates by introducing feedback circuitry that enables reverse operation. This parameter change allows the system to maintain simplicity in internal structure while achieving automated problem-solving through the semi-stochastic search process, avoiding the need for complex self-organizing structures required by memcomputing.
4Ease of manufacture
If conventional computers are used to solve complex problems, then implementation is straightforward, but efficiency and speed are insufficient for hard problems
Solution Approach 1:
The patent creates a composite computing system that integrates classical Boolean logic components with semi-stochastic neural elements. This composite architecture maintains the ease of implementation and manufacturing of conventional computers while dramatically improving productivity for hard problems through the semi-stochastic search mechanism that can escape local minima and find global solutions more efficiently.
Data Source
AI summary
Described herein are methods of and systems for finding solutions to hard problems including factorization, subset sum, maximum satisfiability, bitcoin mining, and many other related and unrelated problems based on a novel type of computing circuits—Boolean-neural hybrids—that combine traditional two- or three-state logic gates with semi-stochastic neurons. Semi-stochastic neurons are a new type of artificial neurons that search for a problem solution stochastically and store the solution deterministically when it is found. Boolean-neural hybrids are based on invertible logic gates and operate in reverse: the input data are applied to the output, and the result is read from the input.