Edge AI, short for edge artificial intelligence, is at the forefront of making devices all around us smarter. Today, development surrounding Edge AI and IoT is progressing at an incredible scale, and it’s become necessary to facilitate a large-scale transition to this new paradigm – and that’s exactly what NVIDIA EGX aims to do. In this article, we will talk about NVIDIA EGX and its transformative role in enabling Edge AI for both better living and smarter industries, on a scale like never seen before!
What is NVIDIA EGX?
NVIDIA EGX is an enterprise platform designed by NVIDIA to facilitate the global transition into an era of accelerated edge computing. Alongside billions of IoT devices already in operation and billions more to be deployed in the coming decade, the prevalence of data and its role has become increasingly important in data analytics and machine learning.
In short, NVIDIA EGX is a one-stop architecture to handle data-intensive workloads in a multitude of scenarios, including data centers, the cloud, and edge computing. In today’s article, however, we will be focusing in particular on leveraging NVIDIA EGX to enable Edge AI.
Edge AI – And Why it is So Important
Edge AI refers to the use of artificial intelligence in the form of machine learning algorithms running directly on edge devices.
Machine learning is a broad field that has seen tremendous progress in recent years. It is based on the principle that a computer can autonomously improve its own performance on a given task by learning from data – sometimes even beyond the capabilities of humans. In fact, machine learning is behind many of the intelligent applications that have been achieved in the recent decade, including image & audio detection, advanced forecasting, and anomaly detection, to name a few.
Understanding the “Edge” in Edge AI
The most exciting prospect of Edge AI lies in its nature of edge computing. To compute on the edge means that data processing occurs on distributed devices that are on the field and in active deployment (or on the edge). Some examples of edge devices include smartphones, as well as various SBCs and microcontrollers.
Take note that this is distinct from cloud computing, where data is sent instead to data centers in a remote location for processing instead. Because of this simple but unique difference in where computing resources are deployed, Edge AI applications have several benefits over the traditional cloud computing paradigm, including:
- Lower Latency Data Processing for Real-Time Insights
- Scalable Computing Power & Infrastructure
- Improved Security & Reliability
In my previous article, I’ve talked more about edge AI and its role in transforming IoT, along with many resources to help you deepen your knowledge. Be sure to click here to learn more!
How does NVIDIA EGX Solve Problems in Edge AI?
Despite the advantages of Edge AI, large-scale implementations are very much in their infancy. Thus, it may be difficult to achieve its full potential in massively scalable applications. Fortunately, this is exactly the problem that NVIDIA is aiming to solve with NVIDIA EGX.
NVIDIA EGX for Edge AI is a comprehensive platform to help businesses deploy and manage scalable machine learning applications on the edge. With it, NVIDIA envisions hospitals, stores, farms and factories that are capable of performing secure, real-time processing with streamed data from trillions of edge sensors. How, you might ask?
1. End-to-End Hardware
NVIDIA EGX provides end-to-end hardware for edge AI through a range of NVIDIA-Certified Systems to run real-time workloads for machine learning applications. Amongst them, the NVIDIA Jetson family was specially designed to run edge AI with powerful but compact GPU-enabled compute on the edge, enabling use-cases like mobile image recognition and sensor fusion.
On the other hand, NVIDIA converged accelerators also allow you to easily integrate AI-capable compute for both data centres and the edge, further bringing traditionally cloud-based capabilities to on-site applications.
2. Enterprise-Ready Software for AI
To enable large-scale AI applications, optimised, scalable software is easily just as important as powerful hardware. NVIDIA EGX provides a full stack software infrastructure to deliver accelerated computing from data centers to the edge! For example, NVIDIA AI Enterprise allows businesses to access NVIDIA’s suite of AI and data analytics software that is fully supported by NVIDIA-Certified systems. This allows you to get your projects off the ground in a rapidly deployable, manageable and scalable environment!
NVIDIA EGX is also compatible with many hybrid-cloud platforms, which provide solutions that combine the use of private networks with more public cloud services. That way, systems are designed with both private security and cloud-level scalability.
The NVIDIA EGX stack also contains the NVIDIA GPU Operator and NVIDIA Network Operator, which aids enterprises in standardizing and automating the provisioning of Kubernetes clusters. Coupled with NVIDIA Fleet Command, massively distributed applications at the edge can even continue to be easily maintained and upgraded with OTA system updates or location health monitoring!
3. Accelerated Enterprise Applications
Last but not least, NVIDIA EGX offers over hundreds of enterprise-level AI-integrated applications, ranging from data analytics to HPC to simulation. These applications have been optimised to be deployed on NVIDIA’s systems, whether on-prem, in the cloud, or on the edge!
Edge AI Applications
Edge AI transforms industries in subtle but tremendously significant ways. By making processes smarter, we’re also making them safer, more efficient, and more fulfilling to work with. Here are some industries that NVIDIA EGX for Edge AI have already begun to transform!
Today’s manufacturing is different from that of even just a decade ago. With more products and more options, the focus has shifted greatly from what used to be low-mix, high-volume manufacturing to high-mix, low-volume manufacturing. Thus, it has become incredibly important to design versatile systems that are adaptable to different requirements and more dynamic as a whole.
Robotics in manufacturing is one of the largest applications of Edge AI. Using vision-based analytics, robots are now no longer programmed with deterministic commands, but instead a generalisable AI algorithm. This allows the same hardware to produce highly customisable products, and also respond to a dynamic environment, performing tasks like quality control or even real-time optimisation!
Cities powered by AI and decisions through advanced analytics – that’s what we refer to as smart cities. To achieve this, edge AI plays a critical role in embedding such applications into our daily living, often making things smarter without us even noticing.
For instance, analytics that rely on embedded sensors are already being implemented to study foot and road traffic patterns, in order to develop better living spaces that minimise congestion and maximise utility. Apart from data-driven decisions, embedded AI applications on the edge now already allow for the widespread implementation of other breakthrough technologies, a personal favourite of mine being autonomous calls to emergency services for vulnerable elderly living alone at home.
AI-enabled healthcare enhances the quality of patient care, increases data security, advances the operational efficiency of healthcare bodies like hospitals and clinics, and reduces the probabilities of error. However, the application of edge AI in healthcare also plays a critical role in proposing what is known as a distributed model of healthcare.
In the future, diagnoses can be performed autonomously in homes without a visit to the doctor, allowing medical professionals to focus their efforts on other critically ill patients. Alongside existing medical equipment, the low latencies of edge AI are also making it possible to deliver real-time medical insights to doctors, enhancing the quality and efficiency of medical response in emergencies!
With climate change, agriculture will face many new and difficult challenges in the coming century. Thankfully, smart agriculture is now enabling farm owners to use various technologies for improving crop yield, controlling irrigation, and monitoring farm performance. With edge AI, we can now take this to the further level.
The growth of crops and rearing of livestock rely on a multitude of factors. While humans can develop intuition for making decisions with experience over time, edge AI offers a more robust data-driven approach. In precision agriculture, the environmental factors on a farm or in a greenhouse can be specifically fine-tuned through a machine learning model, in order to result in the greatest yield. This way, not only is efficiency improved, it might even become possible to achieve effective farm output in adverse climates!
Products in the NVIDIA EGX Edge AI Ecosystem
Edge AI and machine learning is a vast very rapidly developing field with virtually limitless applications in the real world. Fortunately, it’s easy to get started by simply getting to know the products in the NVIDIA EGX Edge AI Ecosystem.
Jetson Family Developer Kits
NVIDIA EGX starts from the NVIDIA Jetson Nano, which is a tiny, power-efficient, but highly capable computer that can provide one-half trillion operations per second (TOPS) of processing for advanced tasks like image recognition. You can purchase a Jetson Nano Developer Kit from as little as $59, and gain access to the NVIDIA JetPack SDK – aGPU-Accelerated software stack for deploying Edge AI applications.
For a more powerful option, you may wish to consider the Jetson Xavier NX, which is also fully supported by NVIDIA EGX and the NVIDIA Jetpack SDK. It’s the most powerful platform offered by NVIDIA for edge development. Equipped with an NVIDIA GPU with 384 CUDA cores and 48 Tensor cores, it can offer astounding performance of 6 TFLOPS (FP16) and 21 TOPS (INT8)!
Jetson Family Modules
The same Jetson developer kits are also available in an SoM form factor, in order to provide hardware that is both modular, flexible, and highly scalable – the NVIDIA Jetson Nano and NVIDIA Jetson Xavier NX Modules. Equipped with a standardised M.2 Key E Connector, these modules are easy to interface with, and can even be clustered together to build highly reliable high-performance GPU clusters for edge AI!
The Jetson Mate is a carrier board for clustering with the Jetson Family modules. Equipped with an onboard 5-port gigabit switch that enables up to 4 SoMs to communicate with each other, as well as independent power for 3 worker/slave nodes, the Jetson Mate with its rich peripherals (CSI, HDMI, USB, Ethernet) and inbuilt fan is a complete solution for building GPU clusters on the edge.
You can now pick up the hardware for a complete edge GPU cluster from Seeed in two convenient packages:
- Jetson Mate Cluster Standard with 1 Jetson Nano SoM and 3 Jetson Xavier NX SoMs
- Jetson Mate Cluster Advanced with 4 Jetson Xavier NX SoMs
In combination with NVIDIA EGX, the Jetson Mate maximises the potential of the Jetson Family in achieving edge AI capabilities!
Finally, NVIDIA AI computing is offered by major cloud service providers, and is architecturally compatible with NVIDIA EGX. Thus, AI applications developed in the cloud are entirely cross compatible with the NVIDIA EGX architecture. Additionally, the NVIDIA Edge Stack allows you to use major cloud IoT services like AWS IoT Greengrass and Microsoft Azure IoT Edge!
Summary & More Resources
The NVIDIA EGX platform is at the forefront of large-scale edge AI implementation. With end-to-end hardware, enterprise software and an extensive library of readily deployable applications, NVIDIA is revolutionising edge AI by making it both more accessible and more effective. Starting from the Jetson Family, affordable hardware now makes it possible for anyone to step towards this new age of computing!
To learn more, you may wish to refer to the following resources:
- Cluster Computing on the Edge – What, Why & How to Get Started
- Building Edge GPU Clusters – Edge Computing Guide
- Edge AI – What is it and What can it do for Edge IoT?
- How Machine Learning has Transformed Industrial IoT