Adaptive Benchmarking Framework Using Exponential Scaling
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Solution Overview
Problem
Benchmarking techniques face inefficiencies due to the need for a large number of tests to achieve accurate results, as performance metrics vary across different tests, leading to wasteful resource consumption.
Innovation Solution
An adaptive benchmarking framework with an adaptive testing state machine that scales benchmarking test lengths exponentially or linearly, evaluating results for proportionate scaling to determine convergent or non-convergent performance metrics, allowing for fewer tests while ensuring accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of benchmarking tests are performed to obtain accurate results, then measurement precision is improved, but loss of time and use of energy increase
Solution Approach 1:
The patent implements feedback by evaluating each benchmarking test result against convergence criteria and using the results to determine whether to continue testing. The adaptive testing state machine monitors benchmarking results and adjusts the number of tests accordingly, stopping when convergence is achieved or criteria are met, thus avoiding unnecessary tests while ensuring accuracy.
Solution Approach 2:
The patent applies dynamics by making the number of benchmarking tests adaptive rather than fixed. The adaptive testing state machine dynamically adjusts the testing process based on real-time evaluation of convergence criteria, allowing the system to perform fewer tests when convergence is rapid and more tests when needed, optimizing the balance between accuracy and time consumption.
2Measurement precision
If a large number of benchmarking tests are performed to obtain accurate results, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The feedback mechanism evaluates benchmarking results against convergence criteria and determines when to stop testing. This prevents unnecessary energy consumption by terminating the testing process once sufficient accuracy is achieved, while still ensuring measurement precision through continuous monitoring of convergence.
Solution Approach 2:
The dynamic adaptive testing approach adjusts the number of tests based on real-time convergence evaluation, optimizing energy usage by performing only the necessary number of tests to achieve accurate results, rather than executing a fixed large number of tests regardless of convergence rate.
3Productivity
If the number of benchmarking tests is reduced to improve efficiency, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The adaptive testing state machine dynamically determines the optimal number of tests by monitoring convergence criteria in real-time. This allows the system to reduce the number of tests when convergence is rapid (improving productivity) while maintaining measurement precision through continuous evaluation of whether the reduced test set suffices for accurate results.
Solution Approach 2:
The feedback mechanism continuously evaluates benchmarking results against convergence criteria to determine when to stop testing. This enables the system to achieve both high productivity (by stopping early when convergence is achieved) and high measurement precision (by ensuring convergence criteria are met before terminating tests).
Data Source
AI summary
Techniques for efficient benchmarking. One method includes obtaining convergent results by performing a benchmarking test with a particular length to obtain a result (time), scaling the time exponentially, performing additional benchmarking tests, obtaining results for those tests, and determining whether the results scale linearly with length. Another method includes obtaining variance for non-convergent results by performing multiple sequences of benchmarking test. Within each new sequence performed, the benchmarking tests are spaced out further apart in time. If new maximum or minimum results are obtained, then further test sequences are performed and if no new maximum or minimum results are obtained after a threshold number of sequences, then the test ends. A device and computer-readable medium for performing benchmarking are also provided herein.