Vision processing with NVIDIA and Jetson at the edge

Why is NVIDIA the hottest stock of 2024?

NVIDIA has made significant strides in integrating Vision Transformers (ViTs) into its AI and computer vision ecosystems. NVIDIA launched on Oct 16, 2024 their new Nemotron model, a fine-tuned version of Llama 3.1 70. Here’s an overview of how NVIDIA is leveraging ViTs and related technologies:

Vision Transformers (ViTs)

1. Overview: Vision Transformers apply transformer architectures, originally designed for natural language processing, to visual data. They excel in capturing long-range dependencies and global context within images, providing advantages over traditional Convolutional Neural Networks (CNNs).
2. Advantages: Parallel Processing: ViTs can process large-scale inputs more efficiently due to their self-attention mechanisms. Robustness: They show improved generalization and robustness against image corruption and noise, making them suitable for challenging real-world applications.
3. Global Context Vision Transformer (GC-ViT): A novel architecture developed by NVIDIA that combines local and global self-attention mechanisms, achieving high accuracy with fewer parameters compared to traditional models.

NVIDIA Tools and Technologies Supporting ViTs

1. NVIDIA TAO Toolkit: A low-code toolkit that simplifies the process of creating and deploying AI models, including those based on ViTs. It enables users to fine-tune pre-trained models on custom datasets efficiently.
2. NVIDIA L4 GPUs: Designed for vision AI workloads, these GPUs support high compute capabilities (e.g., FP8 485 TFLOPs) and are optimized for running ViT workloads. Their energy-efficient design makes them suitable for edge deployments.
3. Computer Vision SDKs: NVIDIA offers a range of SDKs that facilitate the integration of computer vision capabilities into applications. These include libraries for image processing, video analytics, and real-time inference.
4. DeepStream SDK: This SDK enables AI-based multi-sensor processing, allowing developers to build real-time analytics applications using ViTs for tasks such as object detection and tracking.
5. TensorRT:A high-performance deep learning inference optimizer that helps accelerate the deployment of ViT models in production environments, ensuring low latency and high throughput.
6. DALI (Data Loading Library): A library designed to streamline the data preprocessing pipeline for deep learning applications, including those utilizing ViTs, thereby improving overall training efficiency.

Case Study:

John Deere has integrated NVIDIA-based Convolutional Neural Networks (CNNs) into its innovative See & Spray? technology, enabling precise weed detection and targeted herbicide application from tractor-mounted sprayers. Here’s an overview of how this technology works and its implications:

Overview of See & Spray Technology

1. AI-Powered Weed Detection: The See & Spray system uses advanced AI algorithms, powered by NVIDIA GPUs, to analyze images captured by boom-mounted cameras. These cameras scan the field at high speed, processing over 2,100 square feet per second as the tractor moves at speeds of up to 15 mph.
2. Convolutional Neural Networks (CNNs): CNNs are employed to differentiate between crops and weeds in real-time. This deep learning approach allows the system to make rapid decisions about whether to spray herbicide based on the classification of each plant.
3. Precision Spraying: The technology activates individual spray nozzles only when a weed is detected, significantly reducing herbicide usage—by an average of 59% across various crops such as corn, soybeans, and cotton. This targeted application minimizes chemical runoff and environmental impact.
4. Cost Efficiency: Farmers benefit from reduced herbicide costs, with some reporting savings of up to 80%. By paying only for the areas where herbicide is applied, farmers can optimize their operational expenses.
5. Sustainability: The reduction in herbicide use aligns with sustainable farming practices, helping to combat herbicide resistance and promoting better land stewardship.

Technical Implementation

• Hardware: The system utilizes NVIDIA Jetson AGX Xavier modules for on-board processing. These powerful computing units handle the complex image recognition tasks required for real-time decision-making.
• Data Training: John Deere’s technology has been trained on a vast dataset of over a million images to improve accuracy in weed detection. Continuous data collection during field operations helps refine the models further.
• Dual-Tank System: The See & Spray Ultimate model features a dual-tank configuration that allows for simultaneous application of different herbicides or treatments, enhancing operational flexibility.

NVIDIA offers a range of processing and interconnect products, including SDKs and libraries that support various hardware configurations. Here’s an overview of some key products:

Key NVIDIA Processing and Interconnect Products

1. NeMo

• Purpose: A toolkit for building and training state-of-the-art conversational AI models.
• Features: Supports various neural network architectures, including transformers, and provides pre-trained models for quick deployment.
• Hardware Support: Optimized for NVIDIA GPUs to leverage parallel processing capabilities.

2. Riva

• Purpose: An SDK for building AI-powered speech applications.
• Features: Offers capabilities for speech recognition, text-to-speech, and natural language understanding.
• Hardware Support: Designed to run efficiently on NVIDIA GPUs, enabling real-time performance.

3. RAPIDS

• Purpose: A suite of open-source software libraries for data science and analytics.
• Features: Accelerates data processing workflows using GPU acceleration, enabling faster data manipulation and analysis.
• Hardware Support: Works with NVIDIA GPUs to enhance performance in data-intensive tasks.

4. Triton Inference Server

• Purpose: A model inference server that simplifies the deployment of AI models in production environments.
• Features: Supports multiple frameworks (like TensorFlow, PyTorch) and provides dynamic batching for improved throughput.
• Hardware Support: Optimized for NVIDIA GPUs but can also run on CPUs.

5. CUDA-X Libraries

• Purpose: A collection of GPU-accelerated libraries designed to enhance performance across various application domains.

Key Libraries Include:

• cuDNN: For deep learning applications.
• cuBLAS: For linear algebra operations. TensorRT: For high-performance deep learning inference.

6. DOCA SDK

• Purpose: A software development kit designed for the NVIDIA BlueField Data Processing Units (DPUs).
• Features: Provides libraries for networking and data processing, allowing offloading of resource-intensive tasks.
• Hardware Support: Specifically designed for use with BlueField DPUs and ConnectX NICs.

7. Video Codec SDK

• Purpose: Provides APIs for hardware-accelerated video encoding and decoding.
• Features: Supports real-time video processing with low latency, suitable for streaming applications.
• Hardware Support: Utilizes dedicated hardware encoders/decoders present in NVIDIA GPUs.

8. Computer Vision SDKs

• Purpose: Libraries designed to integrate visual perception capabilities into applications.
• Features: Supports a variety of computer vision tasks such as image processing and object detection.
• Hardware Support: Optimized to run on NVIDIA GPUs for enhanced performance.

Hardware Configurations

NVIDIA's products are designed to work across a variety of hardware configurations, including:

• High-performance servers equipped with NVIDIA A100 or A40 GPUs for AI workloads.
• Edge devices utilizing Jetson modules for deploying AI at the edge.
• Cloud environments that leverage NVIDIA GPU instances for scalable processing power.

IBM and NVIDIA are in collaboration:

IBM and NVIDIA are collaborating to accelerate analytics and deploy applications at the edge, leveraging their combined strengths in AI and edge computing technologies. Here’s a detailed overview of their partnership and its implications:

Collaboration Overview

1. IBM Edge Application Manager: This software runs on NVIDIA's EGX platform, enabling the management of applications across numerous edge devices. It allows IT managers to deploy applications or AI models simultaneously to up to 10,000 edge devices, automating lifecycle management.
2. NVIDIA EGX Platform: The EGX platform supports various application frameworks, including NVIDIA Metropolis for smart cities and NVIDIA Aerial for virtual 5G networks. It is designed for high-performance computing at the edge, utilizing NVIDIA GPUs for accelerated processing.
3. Real-Time Insights: By deploying IBM’s Edge Application Manager on NVIDIA's infrastructure, businesses can extract real-time insights from IoT data generated at the edge, reducing latency and improving decision-making capabilities without relying on centralized data processing.

Enhanced AI Adoption

1. AI Workflows: The collaboration aims to streamline AI workflows by integrating IBM Consulting’s expertise with NVIDIA’s AI technologies, such as NVIDIA AI Enterprise software and Triton Inference Server. This combination helps optimize use cases and model training for various industries.
2. Generative AI and Digital Twins: IBM Consulting is leveraging NVIDIA technologies to build digital twin applications using tools like NVIDIA Isaac Sim and Omniverse. These applications enable clients to simulate and optimize real-world assets in a virtual environment.
3. Hybrid Multi-Cloud Architectures: Clients can run applications in diverse hybrid cloud environments, allowing for greater flexibility in how they deploy AI solutions while ensuring compliance and security.

Performance and Scalability

1. NVIDIA H100 Tensor Core GPUs: IBM Cloud now offers access to NVIDIA H100 GPUs, providing significant performance improvements for AI applications, including faster inference times compared to previous models like the A100. This enables enterprises to tackle more demanding AI workloads efficiently.
2. Deployment Automation: IBM Cloud automates the deployment of AI-powered applications, helping organizations reduce errors and improve speed in configuration processes.
3. Data Governance: The partnership emphasizes robust data governance through IBM's watsonx platform, which includes tools for monitoring model performance and ensuring compliance with regulatory standards.

"NVIDIA supplies their latest GPU-optimized pre-built models and application frameworks through NGC, its software catalog. Data scientists and DevOps teams can use these to help accelerate delivery of new ‘ready-to-deploy’ edge analytics models. IBM can then provide IBM Edge Application Manager, which enables autonomous deployment of these models at massive scale on the NVIDIA EGX Edge AI platform. With IBM Edge Application Manager, an IT manager can deploy new applications and AI models simultaneously to up to 10,000?target devices.*?The software automates the work of managing those elements through their lifecycle." [1]

IBM Maximo Visual Inspection requires specific GPU configurations for deep learning model training, particularly NVIDIA GPUs from the Ampere, Turing, Pascal, and Volta architectures. Here’s a summary of the essential details regarding these GPU requirements:

GPU Requirements for Maximo Visual Inspection

Supported Architectures

• NVIDIA Ampere: A10, A100Features: Third-generation Tensor Cores that support various data types, enabling efficient deep learning operations.
• NVIDIA Turing: T4Features: Introduced support for INT8 and INT4 data types, enhancing inference speed.
• NVIDIA Pascal: P40, P100Features: First major architecture with hardware support for FP16 calculations, suitable for deep learning tasks.
• NVIDIA Volta: V100Features: Introduced Tensor Cores specifically designed for deep learning performance improvements.

Memory Requirements

• Minimum GPU Memory: At least 16 GB of GPU memory is required during model training. This is crucial for handling large datasets and complex model architectures effectively.

Unsupported Architectures

• Architectures from other vendors are not supported. This emphasizes the need for NVIDIA GPUs to leverage their specific optimizations for AI and deep learning tasks.

Conclusion

John Deere's integration of NVIDIA-based CNNs into its See & Spray technology represents a significant advancement in precision agriculture. By leveraging deep learning and high-performance computing, this system not only enhances weed management efficiency but also supports sustainable farming practices through reduced chemical usage. As the technology continues to evolve, it promises to deliver even greater benefits for farmers and the environment alike. For optimal performance in IBM Maximo Visual Inspection's deep learning model training, it’s essential to use the specified NVIDIA GPUs with at least 16 GB of memory. The collaboration between IBM's software capabilities and NVIDIA's advanced GPU architectures ensures effective processing and analysis of visual data in various industrial applications.

Caveat:

Opinions expressed are those of the author and not that of IBM Corporation where he works.

References:

1. IBM and NVIDIA: Collaborating to Accelerate Edge Analytics - IBM Blog
2. Application-specific requirements for Maximo Visual Inspection - IBM Documentation