Document Library

This document presents two methods for optimizing the YOLOv4 and YOLOv4-tiny models with customized classes and anchors using Intel® Distribution of OpenVINO™ Toolkit. The object detection use case based on the optimized models is used to verify the corresponding inference results. Furthermore, the decreased inference time of the optimized model is demonstrated with the real-time use case of an ISV.

This guide is to help users install and run Ollama with Open WebUI on Intel Hardware Platform on Windows* 11 and Ubuntu* 22.04 LTS.

The McLaren Island Reference Design is an advanced platform developed by Intel. It features the Intel® Core™ Ultra Mobile Processor (Series 1) and aims to unlock next-generation computing possibilities.

The Rock Island Reference Design is a platform developed by Intel, featuring the 13th Gen Intel® Core™ Mobile Processor, specifically tailored for network and edge applications.

Large Language Models (LLMs) are deep learning algorithms that have gained significant attention in recent years due to their impressive performance in natural language processing (NLP) tasks. However, deploying LLM applications in production has a few challenges ranging from hardware-specific limitations, software toolkits to support LLMs, and software optimization on specific hardware platforms. In this whitepaper, we demonstrate how you can perform hardware platform-specific optimization to improve the inference speed of your LLaMA2 LLM model on the llama.

The OpenVINO™-based C++ demos in Open Model Zoo are built using CMake that allows developers to build simple to complex software across multiple platforms with a single set of input files. In real business use cases, the OpenVINO™ applications are integrated into the end user’s target application and the build steps may vary depending on the build tool in the production setup. However, if the end users are using g++ compiler to build their applications, they do not want to change their build tool. To facilitate this, this paper presents the steps to build the C++ object detection demo using g++ compiler on Ubuntu 20.04.

The Arrow Island Reference Design is an advanced platform developed by Intel, featuring the Intel® Core™ Ultra Mobile Processor (Series 2) aimed at unlocking AI and next-generation possibilities in computing.

Artificial Intelligence (AI) at the Edge is the deployment of AI applications on or close to the device where data is generated or located, as opposed to centralized cloud computing facilities. The edge can be any device outside a central data center, such as network video recorders, security cameras, industrial robots, ultrasound machines, on-prem servers, etc. Instead of sending data to the cloud for processing, AI algorithms are run on the edge device.

The Optical Character Recognition (OCR) involves two stages: text detection and text recognition, to decode the text in the input image. This document presents the steps for optimizing the performance of OCR with the English version of PaddleOCRv3 (en_PP-OCRv3) model using Intel® Distribution of OpenVINO™ Toolkit. The improved accuracy of OCR is demonstrated with the real-time use case of identifying the speaker’s name in videoconferencing. Audio transcription is the process of converting the speaker’s audio into text in videoconferencing. On the other hand, diarization is the process of partitioning an audio stream containing human speech into homogeneous segments according to the identity of each speaker.

The Alder Island Reference Design is a platform developed by Intel, featuring the 12th Gen Intel® Core™ Mobile Processor, specifically tailored for network and edge applications.

Manufacturing in the US is on the rise to satisfy the consumer demand and to mitigate the geopolitical risks. Semiconductor manufacturing industry announced 100’s of billion dollars to start new greenfield factories. Other industries are investing to retrofit their existing legacy infrastructure using latest technologies. This paper discusses how edge computing, 5G private networks, and Artificial Intelligence (AI) technologies can help businesses in digitally transforming their existing factories as well as building new ones.

In the era for multi-workloads consolidations, customers need both agile resource allocation and ensure improved security. These requirements are to protect business critical workloads and adequate compute resources are being allocated for various workloads.