Introduction Arhat is a cross-platform deep learning framework that converts neural network descriptions into lean standalone executable code. Arhat has been designed for deployment of deep learning inference workflows in the cloud and on the edge. Arhat is a vendor-agnostic tool supporting multiple target platforms. Unlike the conventional deep learning frameworks, Arhat translates neural network descriptions directly into platform-specific executable code. This code interacts directly with ...

Analysis of large volumes of legal texts represents a common task in the practice of law firms. Handling this task may require enormous investment of time and effort, therefore significant attention is being paid to automate it using the intelligent software solutions. In this post we describe the early results of our project aiming at prototyping a transformer-based NLP solution for analysis of large volumes of legal texts.

Arhat is a cross-platform framework designed for efficient deployment of deep learning inference workflows in the cloud and at the edge. Arhat translates neural network descriptions directly into lean standalone executable code. Arhat provides interoperability with Intel deep learning software including oneDNN library and OpenVINO toolkit. In this article we discuss design and architecture of Arhat and demonstrate its use for deployment of object detection models for embedded applications.