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US 2025/0155481 A1Published application
Machine learning system and methodology for simulating a mixed analog and digital system using various sets of parameters and estimating their respective power usages and accuracies
AssigneeInvenSense, Inc.
InventorsRemi Poncot, Soufiane Atouani
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Abstract
Disclosed embodiments provide a computer system and methodology for simulating operations of mixed analog and digital systems having various electronics components (for example, MEMS sensors, ADCs, and neural networks) for various sets of parameters, and selecting parameters for these components that would support an optimum pairing of power usage and accuracy in recognizing an activity or event.
Claims20
A computer system, comprising:
- a first component for receiving as input a first set of parameters associated with a MEMS sensor configured to collect analog data associated with an event or an activity and possible values for each parameter of the first set of parameters;
- a second component for receiving as input a second set of parameters associated with an analog-to-digital converter (ADC) configured to convert the analog data into digital data and possible values for each parameter of the second set of parameters, wherein the second set of parameters includes sample rate and resolution;
- a third component for receiving as input a third set of parameters associated with an integrated circuit chip including a feature extraction module and a machine learning model (MLM) configured to recognize the event or the activity by using the digital data and possible values for each parameter of the third set of parameters;
- a simulation module configured to estimate power usage value and accuracy value based on the three sets of parameters using a MLM;
- and
- an identification module to determine one of a maximum estimated accuracy value and information for associated parameters for a given maximum power usage target value or a minimum power usage value and information for associated parameters for a given minimum accuracy target value.
3 more elements
A method, comprising:
- receiving as input a first set of parameters associated with a MEMS sensor configured to collect analog data associated with an event or an activity and possible values for each parameter of the first set of parameters;
- receiving as input a second set of parameters associated with an analog-to-digital converter (ADC) configured to convert the analog data into digital data and possible values for each parameter of the second set of parameters, wherein the second set of parameters includes sample rate and resolution;
- receiving as input a third set of parameters associated with an integrated circuit chip including a feature extraction module and a machine learning model (MLM) configured to recognize the event or the activity by using the digital data and possible values for each parameter of the third set of parameters;
- using simulation to estimate power usage value and accuracy value based on the three sets of parameters and the MLM;
- and
- determining one of a maximum estimated accuracy value and information for associated parameters for a given maximum power usage target value or a minimum power usage value and information for associated parameters for a given minimum accuracy target value.
3 more elements
A computer system, comprising:
- a Machine Learning Model (MLM) trained to facilitate simulations to estimate power usage value and accuracy value upon receiving the below information
- a first set of parameters associated with a MEMS sensor configured to collect analog data associated with an event or an activity and possible values for each parameter of the first set of parameters;
- a second set of parameters associated with an analog-to-digital converter (ADC) configured to convert the analog data into digital data and possible values for each parameter of the second set of parameters, wherein the second set of parameters includes sample rate and resolution;
- and
- a third set of parameters associated with the integrated circuit chip including a feature extraction module and a neural network configured to recognize the event or the activity by using the digital data and possible values for each parameter of the third set of parameters;
- wherein,
- the first and second set of parameters for creating a new database;
- and
- the third set of parameters for training a new MLM.
6 more elements
Dependencies are inferred from the claim text.