Monte Carlo Accelerator for Fast Combinatorial Cost Updates
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Solution Overview
Problem
Existing systems for computing combinatorial cost functions are inefficient and computationally costly, often relying on slow and low-precision numerical methods that struggle to find exact solutions to NP-hard problems.
Innovation Solution
A computing device with an accelerator device and processor is used to generate and process data packs, applying a Monte Carlo algorithm to update variable values and determine transition probabilities, optimizing the combinatorial cost function through specialized hardware like FPGAs or GPUs, and managing dependencies to improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerical methods are used to solve combinatorial cost functions, then approximate solutions can be obtained, but the computation is slow and low-precision
Solution Approach 1:
The patent replaces traditional numerical computation methods with a specialized hardware accelerator that uses Monte Carlo algorithms to compute combinatorial cost functions. This substitution of computational mechanics with dedicated hardware architecture achieves both high precision and fast computation by parallelizing the Monte Carlo simulation process across multiple processing units.
Solution Approach 2:
The patent changes the computational approach from deterministic numerical methods to probabilistic Monte Carlo methods. By using random sampling and statistical convergence, the system achieves high-precision solutions for NP-hard combinatorial optimization problems while maintaining fast computation through hardware acceleration of the stochastic process.
2Measurement precision
If exact solutions are sought for NP-hard combinatorial cost functions, then precision is improved, but computation becomes infeasible
Solution Approach 1:
The patent applies partial action by using Monte Carlo sampling to explore only a representative subset of the solution space rather than exhaustively checking all possible solutions. The randomized sampling strategy with sufficient sample size achieves high-precision results for NP-hard problems while maintaining computational feasibility through hardware acceleration.
Solution Approach 2:
The patent substitutes traditional exhaustive search or deterministic optimization algorithms with a hardware-accelerated Monte Carlo system. This replacement enables exact or near-exact solutions for combinatorial optimization problems to be computed in feasible time by leveraging parallel stochastic simulation in dedicated hardware.
3Productivity
If specialized hardware accelerators are used, then computation speed is improved, but device complexity increases
Solution Approach 1:
The patent segments the computation of combinatorial cost functions into independent Monte Carlo simulation units that can operate in parallel. Each processing unit handles a portion of the random sampling and cost evaluation, allowing the system to achieve high computation speed through parallelization while managing hardware complexity by dividing the workload into modular, identical units.
Solution Approach 2:
The patent designs a universal hardware accelerator architecture that can handle various types of combinatorial optimization problems using the same Monte Carlo framework. The multi-functional design achieves high computation speed across different applications (logistics, machine learning, material design) while avoiding the complexity of designing specialized hardware for each specific problem type.
Data Source
AI summary
A computing device, including memory, an accelerator device, and a processor. The processor may generate a plurality of data packs that each indicate an update to a variable of one or more variables of a combinatorial cost function. The processor may transmit the plurality of data packs to the accelerator device. The accelerator device may, for each data pack, retrieve a variable value of the variable indicated by the data pack and generate an updated variable value. The accelerator device may generate an updated cost function value based on the updated variable value. The accelerator device may be further configured to determine a transition probability using a Monte Carlo algorithm and may store the updated variable value and the updated cost function value with the transition probability. The accelerator device may output a final updated cost function value to the processor.