Clock Frequency Estimation Using Sampled Time Measurement Subsets
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Solution Overview
Problem
Existing methods for clock frequency estimation, such as brute-force calculations, are inefficient and introduce large errors, while simpler solutions that compute cycle-by-cycle frequency may also introduce significant errors.
Innovation Solution
The proposed solution involves using a least squares method to estimate clock cycles or periods, which reduces estimation error by selectively sampling time measurements and using comparisons with previously made time measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute-force calculation of average clock period is used, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent changes the parameter of measurement window size to optimize the trade-off between accuracy and complexity. By using a measurement window that spans multiple clock periods (N periods) but processing only a subset of edges (M edges where M < N), the system achieves high measurement precision without requiring proportionally complex circuitry for processing all edges.
Solution Approach 2:
The patent applies partial action by processing only a subset of clock edges (M out of N total edges) rather than all edges. This selective processing reduces the computational burden and circuit complexity while maintaining accurate frequency estimation through the use of least squares fitting on the selected subset of measurement points.
2Measurement precision
If measurement window size increases, then measurement precision improves, but loss of time increases
Solution Approach 1:
The patent processes only a subset of M edges from the N total edges captured during the measurement window. This partial processing approach reduces the time required for frequency estimation while maintaining accuracy, as the least squares fitting method can achieve precise results with fewer carefully selected measurement points rather than requiring processing of all N edges.
Solution Approach 2:
The patent extracts and processes only the necessary subset of measurement points (M edges) from the complete set of N edges captured during the measurement window. By identifying and processing only the critical subset of edges needed for accurate frequency estimation, the system reduces measurement time while preserving precision.
3Device complexity
If simpler cycle-by-cycle frequency computation is used, then device complexity decreases, but measurement precision worsens
Solution Approach 1:
The patent replaces simple cycle-by-cycle frequency computation with a least squares fitting approach applied to a subset of edges. This substitution maintains relative simplicity while dramatically improving accuracy, as the mathematical fitting method extracts frequency information more effectively from the selected measurement points compared to straightforward cycle counting.
Solution Approach 2:
The patent changes the processing approach from simple cycle counting to least squares frequency estimation applied to a subset of edges. This parameter change in the measurement methodology enables high precision frequency measurement with relatively simple circuitry, avoiding the need for complex processing of all edges while maintaining accuracy.
Data Source
AI summary
An apparatus includes a clock signal input, a sampling circuit, and an estimation circuit. The clock signal input receives N time measurements. A time measurement denotes a respective portion of a given cycle of a clock signal. The sampling circuit is to generate a first and second sampled window from the clock signal input. The first sampled window includes a sum of a plurality of a first m of the N time measurements. The second sampled window includes a sum of a plurality of a last m of the N time measurements. The estimation circuit is to estimate the frequency or period of the clock signal based upon the first sampled window and the second sampled window.