Method and system for multiple time series classification on tiny edge devices using lightweight attention

A system, comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive a lightweight attention network template for time series classification, the lightweight attention network template comprising a set of layers, and wherein the set of layers comprising an embedding network layer, a plurality of lightweight attention modules connected with a scaling layer, and an output network;
• determine a search space associated with the lightweight attention network template, wherein the search space includes a set of search parameters comprising type of the set of layers, configuration of the set of layers, configuration of the plurality of lightweight attention modules, connectivity between the set of layers;
• generate a final lightweight attention network model for deploying in a tiny edge device for time series classification based on one of (i) a k-set of lightweight attention modules, or (ii) a set of m-clones of the k-set of lightweight attention modules, by performing a neural architecture search on the lightweight attention network template based on an optimization technique utilizing the search space and a set of constraints of the tiny edge device, wherein the step of generating the final lightweight attention network model using the k-set of lightweight attention modules comprises:
• randomly sampling a pre-defined set of lightweight attention modules from the plurality of lightweight attention modules using the configuration of the set of layers to obtain a sampled set of lightweight attention modules;
• optimizing the sampled set of attention modules to obtain a k-set of lightweight attention modules based on a module optimization reward, wherein the module optimization reward for each lightweight attention module amongst the k-set of lightweight attention modules is greater than a pre-defined threshold module reward;
• and
• optimizing the lightweight attention network template using the k-set of lightweight attention modules, the configuration of the set of layers, and a labelled input data by maximizing a model optimization reward to obtain the final lightweight attention network model in accordance with the task, wherein the final lightweight attention network model attains a maximum model optimization reward, and wherein the labelled input data is one of (i) a univariate time series signal or (ii) a multivariate time series signal.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a lightweight attention network template for time series classification, the lightweight attention network template further comprising a set of layers, and wherein the set of layers comprising an embedding network layer, a plurality of lightweight attention modules connected with a scaling layer, and an output network;
• determining a search space associated with the lightweight attention network template, wherein the search space includes a set of search parameters further comprising type of the set of layers, configuration of the set of layers, configuration of the plurality of lightweight attention modules, connectivity between the set of layers;
• generating a final lightweight attention network model for deploying in a tiny edge device for the time series classification based on one of (i) a k-set of lightweight attention modules, or (ii) a set of m-clones of the k-set of lightweight attention modules, by performing a neural architecture search on the lightweight attention network template based on an optimization technique utilizing the search space and a set of constraints of the tiny edge device, wherein the step of generating the final lightweight attention network model using the k-set of lightweight attention modules comprises:
• randomly sampling a pre-defined set of lightweight attention modules from the plurality of lightweight attention modules using the configuration of the set of layers to obtain a sampled set of lightweight attention modules;
• optimizing the sampled set of attention modules to obtain a k-set of lightweight attention modules based on a module optimization reward, wherein the module optimization reward for each lightweight attention module amongst the k-set of lightweight attention modules is greater than a pre-defined threshold module reward;
• and
• optimizing the lightweight attention network template using the k-set of lightweight attention modules, the configuration of the set of layers, and a labelled input data by maximizing a model optimization reward to obtain the final lightweight attention network model in accordance with the task, wherein the final lightweight attention network model attains a maximum model optimization reward, and wherein the labelled input data is one of (i) a univariate time series signal or (ii) a multivariate time series signal.