Apparatus and method for optimizing quantifiable behavior in configurable devices and systems

Patent metadata

Field	Value
Publication number	US-11009836-B2
Application number	US-201715457743-A
Country	US
Kind code	B2
Filing date	Mar 13, 2017
Priority date	Mar 11, 2016
Publication date	May 18, 2021
Grant date	May 18, 2021

Abstract

An apparatus and method are provided to perform constrained optimization of a constrained property of an apparatus, which is complex due to having several components, and these components are configurable in real-time. The optimization is achieved by detecting values of the constrained property and a plurality of other properties of the apparatus when the apparatus is configured in a first subset of the plurality of configurations. A model is learned using the detected values of the constrained property. The model represents the constrained property and can also represent other properties as a function of the configurations. The model can also include estimated uncertainties of the constrained property in the model. Then, using the d model and the estimated uncertainties, the optimal configuration can be selected to minimize an error value (e.g., the difference between a desired value and an observed value of the at least one constrained property).

First claim

The invention claimed is:

1. A system, comprising:
an apparatus having at least one constrained property and at least one other property to be optimized, the apparatus including
a plurality of components, which are configurable in real-time, such that the apparatus can be configured in a plurality of configurations, and
detection circuitry configured to detect values of the at least one constrained property and values of the at least one other property when the apparatus is configured in each of a first subset of configurations of the plurality of configurations;

learning circuitry configured to
learn, using a machine learning method and based on the detected values of the at least one constrained property and the detected values of the at least one other property, which were detected for the first subset, a model that predicts values of the at least one constrained property and the at least one other property when the apparatus is configured in each configuration of the plurality of configurations; and

control circuitry configured to
select, using the learned model, a particular configuration of the plurality of configurations that optimizes the at least one other property, subject to a constraint of the at least one constrained property compared to other configurations of the plurality of configurations,
wherein the learning circuitry is further configured to learn the model by comparing the detected values of the at least one constrained property and the at least one other property for the first subset of the plurality of configurations to a database of measurements of other apparatuses also having the at least one constrained property and the at least one other property, the measurements of the other apparatuses representing values of the at least one constrained property and values of the at least one other property of respective other apparatuses when components of the other apparatuses are configured in a plurality of other-apparatus configurations.

2. The system according to claim 1 , wherein the learning circuitry is further configured to encode the learned model as a data structure storing configurations of the apparatus that optimize the at least one other property subject to the constraint of the at least one constrained property.

3. The system according to claim 1 , wherein the learning circuitry is further configured to learn the model using a hierarchical Bayesian method.

4. The system according to claim 1 , wherein
the apparatus is a mobile computing device executing an application,
the plurality of configurations include computational configurations of the mobile computing device,
the control circuitry is included on the mobile computing device, and
the learning circuitry is included on a server, which transmits and receives communications to and from the mobile computing device, and the server stores the database of measurements of other mobile computing devices on a non-transitory computer readable medium, wherein
the database includes measurements of the at least one constrained property and the at least one other property for the other mobile computing devices when the other mobile computing devices are configured in various computational configurations, and
the database is used to learn the model by inference using similarities between the measurements of other mobile computing devices when configured in the various computational configurations and the detected values of the apparatus when configured in the first subset of the plurality of configurations.

5. The system according to claim 1 , wherein
the learning circuitry is further configured to learn the model, wherein the model is represented using performance-frontier configurations of the plurality of configurations, and the performance-frontier configurations correspond to values of the at least one constrained property and values of the at least one other property that are on a convex hull of a tradeoff space between the at least one constrained property and the at least one other property, and
the control circuitry is further configured to select a configuration of the performance-frontier configurations or a combination of configurations of the performance-frontier configurations to be the particular configuration of the plurality of configurations that optimizes the at least one other property.

6. The system according to claim 1 , wherein the control circuitry is further configured to
update the constraint of the at least one constrained property to minimize an error value, wherein the error value represents a difference between a desired value and an observed value of the apparatus, and
update, using the model, the particular configuration of the plurality of configurations that optimizes the at least one other property subject to the updated constraint of the at least one constrained property.

7. The system according to claim 6 , wherein the control circuitry is further configured to
detect updated values of the at least one constrained property and updated values of the at least one other property when the apparatus is configured in the particular configuration of the plurality of configurations;
update the model based on the particular configuration of the plurality of configurations and on the updated values of the at least one constrained property and the updated values of the at least one other property, and
perform the updating of the configuration of the plurality of configurations using the updated model together with the updated constraint of the at least one constrained property.

8. The system according to claim 4 , wherein the control circuitry is further configured to select the particular configuration of the plurality of configurations, wherein
the at least one other property includes one of an energy-consumption rate and a computational-performance rate,
when the at least one other property includes the computational-performance rate, the at least one constrained property includes the energy-consumption rate,
when the at least one other property includes the energy-consumption rate, the at least one constrained property includes the computational-performance rate, and
the energy-consumption rate is a rate at which the mobile computing device consumes energy while executing the application, and the computational-performance rate is a rate at which the mobile computing device performs computational tasks of the application.

9. The system according to claim 8 , wherein, when the at least one constrained property includes the computational-performance rate, the control circuitry is further configured to select the configuration of the plurality of configurations to be a schedule that minimizes the energy consumed over a predefined time period subject to a constraint that a predefined number of the computational tasks of the application are completed within the predefined time period, wherein
the schedule includes that
the apparatus is configured in a first configuration of the plurality of configurations during a period corresponding to a duty cycle of the schedule,
the apparatus is configured in a second configuration of the plurality of configurations, which is different from the first configuration, for a period corresponding to a complement of the duty cycle, and
the first configuration and the second configuration respectively correspond to the values of the at least one constrained property and the values of the at least one other property of the model that are on a convex hull of a trade-off space between the at least one constrained property and the at least one other property.

10. The system according to claim 2 , wherein
the learning circuitry is further configured to
learn t

Assignees

• Univ Chicago

Inventors

• Hoffmann Henry
• Lafferty John
• MISHRA Nikita

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G05B13/0265 Primary

  the criterion being a learning criterion · CPC title

• G06N7/005

  Physics · mapped topic