EFFICIENT CONTROL OF POWER CONSUMPTION IN PORTABLE SENSING DEVICES

• US 20130124891A1
• Filed: 07/15/2012
• Published: 05/16/2013
• Est. Priority Date: 07/15/2011
• Status: Abandoned Application

• Current Assignee: JB IP Acquisitions LLC
• Original Assignee: AliphCom, Inc. (AliphCom Corp.)
• Inventors: Donaldson, Thomas Alan
• Application Number:
  US13/549,509
• Publication Number:
  US 20130124891A1
• Time in Patent Office: Days
• US Class Current: 713/322
• CPC Class Codes: G06F 1/163 Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 1/324 by lowering clock frequency
• CPC Class Codes: G06F 1/325 Power saving in peripheral device
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The various embodiments of the invention relate generally to portable devices and systems, including wearable devices, that include sensors that are used for sensing the physiological, emotional and/or environmental condition of a person carrying, wearing or otherwise using the device or system and more specifically, to an architecture and method reducing the power consumption of such devices and systems that include one or more sensors. In an embodiment, a wearable device includes one or more sensors, sensor data power optimization controller, a power-clock controller, a memory optimizer and a sensor optimizer.

Claims

1. A method to optimize power in wearable or portable devices, the method comprising:

determining a mode of operation of a wearable device in which sensor data is generated by one on or more sensors, the mode of operation associated with data representing processing requirements for a sensor-related operation;

selecting a clock frequency based on an amount of remaining processing cycles of a processor to achieve a target amount of processing cycles performed by the processor for the mode of operation;

allocating an amount of active circuitry in either a first portion of circuitry or a second portion of circuitry as a function of a measure of predicted usage of the data generated by the processor; and

modifying data collection characteristics to determine a subset of data collection characteristics with which the sensor data is consumed by the processor.

2. The method of claim 1, wherein selecting the clock frequency, allocating the amount of active circuitry, and modifying the data collection characteristics reduces power consumption that is otherwise drawn from a battery in the wearable device.

3. The method of claim 1, further comprising:

operating the processor in a switching mode of operation.

4. The method of claim 3, wherein operating the processor in the switching mode of operation further comprises:

operating the processor at a first voltage and at a first clock frequency during a first portion of the sensor-related operation; and

operating the processor at a second voltage and at a second clock frequency during a second portion of the sensor-related operation.

5. The method of claim 3, wherein operating the processor in the switching mode of operation further comprises:

receiving data representing the amount of remaining processing cycles and an amount of time remaining for a timeframe in which the sensor-related operation is performed; and

determining a next clock frequency to establish an efficiency value.

6. The method of claim 5, wherein determining the next clock frequency further comprises:

determining an operating voltage value for the next clock frequency to establish the efficiency value; and

updating the clock frequency with the next clock frequency.

7. The method of claim 1, wherein allocating the amount of active circuitry further comprises:

allocating an amount of memory in either volatile memory or non-volatile of memory as, a function of the measure of predicted usage.

8. The method of claim 7. Wherein allocating the amount of the memory further comprises:

determining a first amount of power to write the data generated by the processor to the non-volatile memory;

determining an amount of time in which the processor is to rewrite the data;

determining a second amount of power to store the data in the volatile memory for the amount of time; and

allocating the amount of memory based on the least power consumed of either the first or the second amounts of power.

9. The method of claim 1, wherein allocating the amount of active circuitry further comprises:

allocating an amount of memory based on whether a reference object associated with the data generated by the processor is similar to other data stored in either volatile memory or non-volatile of memory; and

storing the data in an allocated amount of memory in which the other data shares the same reference object.

10. The method of claim 9, wherein the reference object includes data specifying a relationship in which the sensor-related operation uses the data and the other data.

11. The method of claim 1, wherein allocating the amount of active circuitry further comprises:

allocating an amount of memory comprising;

determining an amount of time in which the data generated by the processor is to be stored; and

storing the data in a first memory bank if the amount of time is equivalent to a first time period, or

storing the data in a second memory bank if the amount of time is equivalent to a second time period, the second time period being greater than the first time period,

wherein data that are stored for longer periods of time are stored in memory banks having smaller sizes than data that are stored for shorter periods of time.

12. The method of claim 1, wherein the data collection characteristics comprises a sampling rate, a bit-depth and/or a buffer size.

13. The method of claim 12, wherein modifying the data collection characteristics comprises:

maximizing an amount of the sensor data retrieved from the one or more sensors per unity of energy consumed.

14. The method of claim 12, wherein modifying the data collection characteristics comprises:

determining data representing an information metric.

15. The method of claim 14, wherein the information metric includes data representing a value of the sampling rate, a value of the bit-depth and a size of the buffer configured to provide an optimal value of power efficiency for the wearable device.

16. A wearable device comprising:

one or more sensors; and

a sensor data power optimization controller configured to optimize a measure of energy efficiency indicative of the power consumed by one or more circuits activated per unit of processing, the sensor data power optimization controller comprising;

a power-clock controller configured to select a clock frequency based on an amount of remaining processing cycles of a processor to achieve a target amount of processing cycles performed by the processor and an amount of time remaining for a timeframe in which the processor performs a sensor-related operation;

a memory optimizer configured to allocate an amount of memory in either volatile memory or non-volatile of memory based on the least power consumed of using either the volatile memory or the non-volatile of memory; and

a sensor optimizer configured to determine a subset of data collection characteristics with which the sensor data is retrieved for used by the processor.

17. The wearable device of claim 16, wherein power-clock controller comprises:

a clock rate controller configured to generate an updated clock frequency for the clock frequency based on the amount of remaining processing cycles and an amount of time remaining for a timeframe;

a multi-rate clock generator configured to generate the updated clock frequency; and

a variable power supply configured to generate a voltage level in which the voltage level and the updated clock frequency provides an optimal value of efficiency.

18. The wearable device of claim 16, wherein the memory optimizer comprises:

an allocator configured to allocate the memory as a function of a measure of predicted usage of the data generated by the processor, and further configured to allocate the memory based on whether a reference object associated with the data generated by the processor is similar to other data stored in either the volatile memory or the non-volatile of memory; and

a usage manager configured to enable memory banks and deallocate the memory based on the invalidity of the reference object.

19. The wearable device of claim 16, wherein the sensor optimizer comprises:

an information metric determinator configured to determine information metric data representing a value of the sampling rate, a value of the hit-depth and size of a buffer configured to provide an optimal value of power efficiency for the wearable device.

20. The wearable device of claim 16, wherein the processor comprises:

a sensor data processor.