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Edge-Optimized Deep Learning: Harnessing Generative AI and Computer Vision with Open-Source Libraries

Samet Akcay · Paula Ramos Giraldo · Ria Cheruvu · Alexander Kozlov · Zhen Zhao · Zhuo Wu · Raymond Lo · Yury Gorbachev

Summit 436
[ ] [ Project Page ]
Tue 18 Jun 8:30 a.m. PDT — 5 p.m. PDT


This tutorial aims to guide researchers and practitioners in navigating the complex deep learning (DL) landscape, focusing on data management, training methodologies, optimization strategies, and deployment techniques. It highlights open-source libraries like the OpenVINO toolkit, OpenVINO Training eXtensions (OTX), and Neural Network Compression Frameworks (NNCF) in streamlining DL development. The tutorial covers how OTX 2.0 simplifies the DL ecosystem (Computer Vision) by integrating various frameworks and ensuring a consistent experience across different platforms (MMLab, Lightning, or Anomalib). It also demonstrates how to fine-tune generative AI models, specifically Stable Diffusion SD with LoRA, and the benefits of customized models in reducing latency and enhancing efficiency. The tutorial explores fine-tuning visual prompting tasks, including Segment Anything Model (SAM). It explains how to fine-tune a SD model with custom data using multiple acceleration methods, and how to deploy the fine-tuned model using OpenVINO Transformation Passes API. Lastly, the tutorial focuses on model optimization capabilities for the inference phase, with the OpenVINO toolkit and OTX library integrating with NNCF to refine neural networks and improve inference speed, especially on edge devices with limited resources. The tutorial includes demos showcasing how OpenVINO runtime API enables real-time inference on various devices.

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