Abstract: This disclosure relates generally to a method and system for latency optimized heterogeneous deployment of convolutional neural network (CNN). State-of-the-art methods for optimal deployment of convolutional neural network provide a reasonable accuracy. However, for unseen networks the same level of accuracy is not attained. The disclosed method provides an automated and unified framework for the convolutional neural network (CNN) that optimally partitions the CNN and maps these partitions to hardware accelerators yielding a latency optimized deployment configuration. The method provides an optimal partitioning of the CNN for deployment on heterogeneous hardware platforms by searching network partition and hardware pair optimized for latency while including communication cost between hardware. The method employs performance model-based optimization algorithm to optimally deploy components of a deep learning pipeline across right heterogeneous hardware for high performance.

Abstract: State of the art techniques used for document processing and particularly for handling processing of images for data extraction have the disadvantage that they have large computational load and memory footprint. The disclosure herein generally relates to text processing, and, more particularly, to a method and system for generating a data model for text extraction from documents. The system prunes a pretrained base model using a Lottery Ticket Hypothesis (LTH) algorithm, to generate a LTH pruned data model. The system further trims the LTH pruned data model to obtain a structured pruned data model, which involves discarding filters that have filter sparsity exceeding a threshold of filter sparsity. The structured pruned data model is then trained from a teacher model in a Knowledge Distillation algorithm, wherein a resultant data model obtained after training the structured pruned data model forms the data model for text detection.