Data Storage Power Management via Event-Based Estimation
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Solution Overview
Problem
Existing data storage devices face challenges in efficiently managing power consumption, which affects battery life and operating temperatures, and requires lengthy characterization procedures to determine optimal power budgets.
Innovation Solution
A data storage device comprising a non-volatile memory, hardware processing devices, a power sensor, and a processor that determines power-per-processing event values and controls power delivery to optimize power usage within a defined budget.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional power management methods are used in data storage devices, then power consumption can be controlled to some extent, but lengthy characterization procedures are required to determine optimal power budgets
Solution Approach 1:
The system performs self-characterization by automatically measuring its own power consumption across different operational states and using these measurements to determine power-per-processing-event values, eliminating the need for external characterization procedures and manual power budget determination
Solution Approach 2:
The system implements a feedback mechanism where power consumption measurements from the power sensor are continuously fed back to the controller, which then adjusts power allocation based on actual measured values, enabling adaptive power management without pre-characterization
2Duration of action of moving object
If power consumption is reduced to maximize battery life, then battery life is extended, but operating temperatures may be affected and performance may be limited
Solution Approach 1:
The system dynamically adjusts power allocation to processing devices based on real-time power consumption measurements and operational requirements, allowing the device to operate at higher performance levels when power is available and conserve power when battery life is prioritized, rather than using fixed power limits
Solution Approach 2:
The system changes power allocation parameters adaptively based on measured power consumption and operational state, adjusting the power-per-processing-event values to optimize the balance between battery life and performance based on current conditions
3Measurement precision
If detailed power measurements are taken for each processing device, then accurate power control is achieved, but the complexity of the power management system increases
Solution Approach 1:
The system uses a power sensor as an intermediary to measure total power consumption, and the controller acts as a mediator that processes these measurements and calculates individual device power consumption using pre-determined power-per-processing-event values, avoiding the need for complex individual sensing circuits for each device
Data Source
AI summary
Methods and apparatus for power management in data storage devices are provided. One such data storage device (DSD) includes a non-volatile memory (NVM), a set of hardware processing engines, and a power sensor to detect a total power consumption of the set of hardware processing engines. A processor is configured to determine a power-per-processing event value for each of the set of processing engines based on total power consumption measurements, then control delivery of power to the processing engines based on the power-per-processing event values in accordance with a power budget. In some examples, the DSD employs a least-squares procedure to estimate the power-per-processing event values so the values can be determined without needing to measure the individual power consumption of the processing engines. Exemplary processing engines include a Read engine, a Write engine, etc. A recursive least-squares update procedure is also described.