Low-Power Memory Buffer for Adaptive Signal Sampling
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Solution Overview
Problem
In resource-constrained systems like wireless sensor networks, there is a tradeoff between maximizing time spent in sleep states to conserve power and minimizing data loss associated with important events, as existing systems may lose data captured before, during, or shortly after a wakeup event.
Innovation Solution
A system for adaptive and compressed sampling that asynchronously samples and stores input signals during sleep states, using a low-power memory buffer to store only maximum and minimum values, allowing for signal reconstruction after a wakeup event without sacrificing energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the system spends more time in sleep states to conserve power, then energy efficiency is improved, but data loss increases
Solution Approach 1:
The system performs preliminary sampling and stores signal data in a buffer memory before the wakeup event occurs. This preliminary action ensures that data is captured and preserved during the sleep state, preventing data loss when the system wakes up. The buffer memory holds the sampled data ready for processing after wakeup, resolving the contradiction between power savings and data preservation.
2Loss of information
If traditional sampling methods are used during sleep state, then data capture is improved, but power consumption increases
Solution Approach 1:
The invention extracts only the essential sampling function and stores it in a dedicated buffer memory that operates independently during sleep state. By separating the sampling function from the main processing system, the patent enables minimal power consumption while maintaining data capture capability. The buffer memory holds the extracted essential data (maximum and minimum values) without requiring full system operation.
Solution Approach 2:
The system changes the sampling parameters during sleep state by reducing the sampling rate and storing only extreme values (maximum and minimum) rather than continuous data. This parameter change significantly reduces power consumption while still capturing the essential signal characteristics needed for accurate reconstruction after wakeup.
3Measurement precision
If more samples are stored for accurate signal reconstruction, then measurement precision is improved, but memory requirements increase
Solution Approach 1:
The invention extracts only the critical signal characteristics (maximum and minimum values) rather than storing complete signal waveforms. This extraction approach maintains measurement precision for signal reconstruction while dramatically reducing the quantity of data that needs to be stored in memory, thus resolving the contradiction between accuracy and memory requirements.
Solution Approach 2:
The system changes the sampling strategy by storing only extreme values (maximum and minimum) at reduced sampling rates rather than continuous high-resolution data. This parameter change in the sampling methodology achieves sufficient reconstruction accuracy for most applications while minimizing memory capacity requirements.
Data Source
AI summary
Aspects of a low power memory buffer are described. In one embodiment, a sampling rate of a signal is adjusted to identify extrema of a signal. An extrema pulse is generated and, in response to the extrema pulse, a time segment and potential value of the signal are stored in a memory. In other aspects, rising and falling slopes of the signal are tracked to identify a local maximum and a local minimum of the signal. In this scenario, an extrema pulse is generated for each of the local maximum and minimum, and time segment and potential values are stored for the local maximum and minimum. Generally, the storage of analog values of the signal at an adjusted sampling rate is achieved with low power, and the signal may be reconstructed at a later time.