Patent ID: 11967133
Assignee: TATA CONSULTANCY SERVICES LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A system for cooperative and cascaded inference on edge, the system comprising:
a memory storing instructions;
one or more Input/Output (I/O) interfaces; and
one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
build an integrated Deep Learning (DL) model for an edge device by stitching a first subsequence of layers of a classifier to a DL model, wherein the DL model comprises a second subsequence of layers corresponding to a shared feature extractor and a localizer,
wherein the first subsequence of layers and the second subsequence of layers are split based on a) a Host Processing Elements (HPE) and b) a Guest Processing Elements (GPE) comprising,
wherein the second subsequence of layers corresponding to the shared feature extractor are executed in the GPE providing low latency, and the second subsequence of layers corresponding to the localizer and the first subsequence of layers corresponding to the classifier are executed in the HPE,
wherein the shared feature extractor a) extract features from an input image to transform the input image from a spatial domain to a feature space and b) shares the extracted features to the localizer and the classifier, and
wherein the localizer is triggered to localize an object in the input image only if the classifier classifies the input image as positive, indicating presence of the object in the input image; and

train the integrated DL model for a cooperative cascaded inference to indicate presence of the object in the input image through a multi-decision output with simultaneous classification and localization of the object on the edge device, wherein the localizer is triggered to localize the object in the input image only if the classifier classifies the input image as positive, indicating presence of the object in the input image, and wherein training comprises:
performing joint learning of the integrated DL model utilizing a Multitask Learning (MTL) approach, wherein the classifier learns a classification task and the localizer learns a localization task based on an integrated loss function (L) that minimizes a classifier loss function tc and a localizer loss function tl in unison using a relative task weight parameter α.