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Read how the power of AI and edge computing is critical to driving operational efficiencies and productivity gains.

Automation and monitoring of industrial assets, systems, processes, and environments are increasingly important across manufacturing industries, including transportation, electronics, mining, and textiles. In order to implement safer and more productive practices, companies are automating their manufacturing processes with IoT sensors. IoT sensors generate vast amounts of data that, when combined with the power of AI, produce valuable insights that manufacturers can use to improve operational efficiency. 

Edge computing allows sensor-enabled devices to collect and process data locally to deliver insights on the factory floor without having to communicate with the cloud. Edge AI enables any device or computer to process data and make AI-led decisions in real time, with minimal latency. This convenience gives rise to new use cases where fast, real-time insights are required, like when scanning for product defects on assembly lines, identifying workplace hazards, flagging machines that require maintenance, and more.

By bringing AI processing tasks closer to the source, edge computing provides many advantages to manufacturers, including:

• Ultra-Low Latency Processing: In manufacturing scenarios, throughput is critical.  Inspection processes can be a key bottleneck in the overall process. Processing data at the edge saves valuable microseconds as the data does not need to be sent to and from the cloud.
• Enhanced Security: A manufacturer’s data is key IP. Keeping data within the device compared to sending it through the cloud means that it stays secure and is less vulnerable to attacks or data breaches.
• Bandwidth Savings: Sending only AI processed smart data to the cloud and processing the remaining high velocity (for example, vibration) and high volume (for example, image and video) data locally on the device lowers data transmission rates and frees up bandwidth, cutting costs.
• Harnessing OT Domain Knowledge: Empowering OT domain experts to control the data processing AI parameters by leveraging their tacit knowledge enables them to create a highly adaptive and outcome focused agile solution.
• Robust Infrastructure: Processing data on site through edge devices allows companies to keep their manufacturing processes moving without disruption, even if network outages occur. 

Use Cases of Edge Computing in Manufacturing

Manufacturers globally have started to use AI at the edge to transform their manufacturing processes. The following use cases explore how edge computing is promoting enhanced efficiency and productivity in manufacturing. 

• Predictive Maintenance: Sensor data can be used to detect anomalies early and predict when a machine will fail. Sensors on equipment scan for flaws and alert management if a machine needs a repair so the issue can be addressed early, avoiding downtime. The combination of sensor data, AI, and edge computing accurately assesses equipment condition and allows the manufacturer to avoid costly unplanned downtime. For example, sensor-equipped video cameras in chemical plants are used to detect corrosion in pipes and alert staff before they can cause any damage.
• Quality Control: Defect detection is an essential part of the manufacturing process. When running an assembly line where millions of products are made, defects need to be caught in real time. Devices that use edge computing can make decisions in microseconds, catch defects instantly, and alert staff. This capability provides a significant advantage to factories as it can reduce waste and improve manufacturing efficiency.
• Equipment Effectiveness: Manufacturers are continuously looking to improve processes. When combined with sensor data, edge computing can be used to assess overall equipment effectiveness. For example, in the automotive welding process, manufacturers need to meet many requirements to ensure that their welding is of the highest quality. Using sensor data and edge computing, companies can monitor the in real time, and catch defects or  safety risks before products leave the factory.
• Yield Optimization: In food production plants, it is critical to know the exact quantity and quality of the ingredients being used in the manufacturing process. By using sensor data, AI, and edge computing, machines can recalibrate instantly if any parameters need to be changed in order to produce better quality products. There is no need for manual supervision, or to send data to a central location for review. The sensors on site are capable of making decisions in real time to improve yields.
• Factory Floor Optimization: Manufacturers must understand how factory spaces are being used in order to improve processes. For example, in a car manufacturing plant it is inefficient if workers must walk to different locations to complete tasks. Supervisors may be unaware of this bottleneck if the data is not available. Sensors help analyze factory spaces—how are they being used, who is using them and why. Data and critical Edge AI processed information is sent to a central location for a supervisor to review. The supervisor can then make informed optimizations to factory processes.
• Supply Chain Analytics: There is a growing need for companies to have constant visibility on procurement, production, and inventory management. By automating these processes with AI and edge computing, companies can better predict and manage their supply chain. For example, an electronic manufacturing company with automated  processes can immediately alert other production facilities across the country to generate more of a needed raw material so production is not affected.
• Worker Safety: Industrial workers often operate heavy machinery and handle hazardous materials at manufacturing sites. Using a network of cameras and sensors equipped with AI-enabled video analytics, manufacturers can identify workers in unsafe conditions and quickly intervene to prevent accidents. Edge computing is critical to worker safety since life-saving decisions need to be made in real time. 

Edge computing will continue to transform the manufacturing industry by bringing about AI-driven operational efficiencies and productivity gains. Download this free e-book to learn how edge computing is helping build smarter and safer spaces around the world.

A new study creates a deep convolutional neural network using global satellite imagery to detect sustainable roofscapes—a promising strategy for climate mitigation.

A new AI-mapping tool is helping scientists assess how cities across the globe are using rooftops to combat climate change. Named Roofpedia, the research creates an open-source and scalable map of sustainable rooftops—a promising strategy for climate mitigation. Identifying areas with solar or green installations could help guide urban development, while also boosting community health, prosperity, and the environment.

“By collecting such data, Roofpedia allows us to gauge how cities might further utilize their rooftops to mitigate carbon emissions and how much untapped potential their roofscapes have,” coauthor Filip Biljecki, an assistant professor at the National University of Singapore and principal investigator for the Urban Analytics Lab, said in a press release. 

By 2050, an estimated 68% of the world’s population will be living in urban areas. Strategies focused on the well-being of people and the planet remain central to creating healthy and thriving communities in the future. Studies have shown the benefits of sustainable rooftops—most commonly roofs with solar or green space— are plentiful and wide-ranging. Beyond creating an efficient and relatively clean source of energy, they also reduce carbon emissions, add to food production, aid stormwater management, improve air quality, create wildlife habitat, reduce traffic noise, and promote biodiversity.

Accounting for current installations could help planners and developers benchmark spaces, identify new potential areas, gauge the effectiveness of current incentives such as subsidies, and track the progress of environmentally friendly businesses.

Despite the advantages, most research focuses on estimating the potential of these roofscapes, rather than their distribution globally. A lack of data also limits the understanding of current totals, distribution, and global growth. 

The team sought out to fill this data gap with the creation of Roofpedia, an open registry of sustainable roofscapes around the world.

The tool was created using high-resolution satellite imagery from Mapbox, with images from 17 diverse cities across Europe, North America, Australia, and Asia. Sampling a range of urban contexts and image conditions, the team hand-labeled some of the satellite images and trained a convolutional neural network to identify and tag roofs with solar, green space, or both.  

According to study co-author Abraham Wu, Roofpedia was developed on PyTorch using NVIDIA Docker and an NVIDIA RTX 2060 GPU.

As data is added to the Roofpedia Registry and Index, the tool maps the distribution of solar and green rooftops and calculates the rank of a city. Scores and ranks are calculated by comparing solar and green roofs against the total number of buildings and area of buildings in a city, along with an overall combined score.

Currently, Las Vegas leads the solar ranking with a score of 86, while Zurich scores high in both solar (81) and green (100), for a combined score of 91. 

There are over a million buildings in the current data set and the researchers note that more cities are being added as aerial or satellite imagery becomes available. Final results are rendered on Mapbox, with both the dataset and code available on GitHub.

“We are making this sustainable roofscape inventory available publicly, aiding researchers, practitioners, local governments, and the public to understand the current status of the roofscape in the context of sustainable urban development and achieving carbon neutrality,” the researchers write in the study.

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Read the full article in Landscape and Urban Planning >>
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Imagine trying to make contact with super-intelligent beings. Now imagine they’re not from space. Sperm whales, the largest of the toothed whales, boast enormous brains, explains David Gruber, a professor of biology and environmental science at the City University of New York. Their brains weigh 20 pounds compared to our three-pounders. Perhaps more important: their Read article >

The post Whale Hello There: NVIDIA Intern Part of Team Working to Understand, Communicate with Whales appeared first on The Official NVIDIA Blog.

Highlighting NVIDIA’s fast-growing impact, Time magazine Wednesday named NVIDIA CEO Jensen Huang to its list of most influential people of 2021. NVIDIA has enabled a revolution that “allows phones to answer questions out loud, farms to spray weeds but not crops, doctors to predict the properties of new drugs—with more wonders to come,” Andrew Ng Read article >

The post NVIDIA to Drive “Advances for Decades to Come,” Time Magazine Writes appeared first on The Official NVIDIA Blog.

A multi-hospital initiative sparked by the COVID-19 crisis has shown that, by working together, institutions in any industry can develop predictive AI models that set a new standard for both accuracy and generalizability. Published today in Nature Medicine, a leading peer-reviewed healthcare journal, the collaboration demonstrates how privacy-preserving federated learning techniques can enable the creation Read article >

The post Medical AI Needs Federated Learning, So Will Every Industry appeared first on The Official NVIDIA Blog.

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This post defines NICs, SmartNICs, and lays out a cost-benefit analysis for NIC categories and use cases.

This post was originally published on the Mellanox blog.

Everyone is talking about data processing unit–based SmartNICs but without answering one simple question: What is a SmartNIC and what do they do?

NIC stands for network interface card. Practically speaking, a NIC is a PCIe card that plugs into a server or storage box to enable connectivity to an Ethernet network. A DPU-based SmartNIC goes beyond simple connectivity and implements network traffic processing on the NIC that would necessarily be performed by the CPU, in the case of a foundational NIC.

Some vendors’ definitions of a DPU-based SmartNIC are focused entirely on the implementation. This is problematic, as different vendors have different architectures. Thus, a DPU-based SmartNIC can be ASIC–, FPGA–, and system-on-a-chip-based. Naturally, vendors who make just one kind of NIC insist that only their type of NIC should qualify as a SmartNIC.

There are various tradeoffs between these different implementations with regards to cost, ease of programming, and flexibility. An ASIC is cost-effective and may deliver the best price performance, but it suffers from limited flexibility. An ASIC-based NIC, like the NVIDIA ConnectX-5, can have a programmable data path that is relatively simple to configure. Ultimately, that functionality has limits based on what functions are defined within the ASIC. That can prevent certain workloads from being supported.

By contrast, an FPGA NIC, such as the NVIDIA Innova-2 Flex, is highly programmable. With enough time and effort, it can be made to support almost any functionality relatively efficiently, within the constraints of the available gates. However, FPGAs are notoriously difficult to program and expensive.

For more complex use cases, the SOC, such as the Mellanox BlueField DPU-programmable SmartNIC provides what appears to be the best DPU-based SmartNIC implementation option: good price performance, easy to program, and highly flexible.

Focusing on how a particular vendor implements a DPU-based SmartNIC doesn’t address what it’s capable of or how it should be architected. NVIDIA actually has products based on each of these architectures that could be classified as DPU-based SmartNICs. In fact, customers use each of these products for different workloads, depending on their needs. So the focus on implementation—ASIC vs. FPGA vs. SoC—reverses the ‘form follows function’ philosophy that underlies the best architectural achievements.

Rather than focusing on implementation, I tweaked this PC Magazine encyclopedia entry to give a working definition of what makes a NIC a DPU-based SmartNIC:

DPU-based SmartNIC: 
A DPU-based network interface card (network adapter) that offloads processing tasks that the system CPU would normally handle. Using its own onboard processor, the DPU-based SmartNIC may be able to perform any combination of encryption/decryption, firewall, TCP/IP, and HTTP processing. SmartNICs are ideally suited for high-traffic web servers.

There are two things that I like about this definition. First, it focuses on the function more than the form. Second, it hints at this form with the statement, “…using its own onboard processor… to perform any combination of…” network processing tasks. So the embedded processor is key to achieving the flexibility to perform almost any networking function.

You could modernize that definition by adding that DPU-based SmartNICs might also perform network, storage, or GPU virtualization. Also, SmartNICs are also ideally suited for telco, security, machine learning, software-defined storage, and hyperconverged infrastructure servers, not just web servers.

NIC categories

Here’s how to differentiate three categories of NICs by the functions that the network adapters can support and use to accelerate different workloads:

Here I’ve defined three categories of NICs, based on their ability to accelerate specific functionality:

• Foundational NIC
• Intelligent NIC (iNIC)
• DPU-based SmartNIC

The foundational, or basic NIC simply moves network traffic and has few or no offloads, other than possibly SRIOV and basic TCP acceleration. It doesn’t save any CPU cycles and can’t offload packet steering or traffic flows. At NVIDIA, we don’t even sell a foundational NIC any more.

The NVIDIA ConnectX adapter family features a programmable data path and accelerates a range of functions that first became important in public cloud use cases. For this reason, I’ve defined this type of NIC as an iNIC, although today on-premises enterprise, telco, and private clouds are just as likely as public cloud providers to need this type of programmability and acceleration functionality. Another name for it could be smarterNIC without the capital “S.”

In many cases, customers tell us they need DPU based SmartNIC capabilities that are being offered by a competitor with either an FPGA or a NIC combined with custom, proprietary processing engines. But when customers really look at the functions they need for their specific workloads, ultimately they decide that the ConnectX family of iNICs provides all the function, performance, and flexibility of other so-called SmartNICs at a fraction of the power and cost. So by the definition of SmartNIC that some competitors use – our ConnectX NICs are indeed SmartNICs, though we might call them intelligent NICs or smarter NICs. Our FPGA NIC (Innova) is also a SmartNIC in the classic sense, and our SoC NIC (using BlueField) is the smartest of SmartNICs, to the extent that we could call them Genius NICs

So, what is a SmartNIC? A DPU-based SmartNIC is a network adapter that accelerates functionality and offloads it from the server (or storage) CPU.

How you should build a DPU-based SmartNIC and which SmartNIC is the best for each workload… well, the devil is in the details. It’s important to dig into exactly what data path and virtualization accelerations are available and how they can be used. If you’re interested, see my next post, Achieving a Cloud-Scale Architecture with DPUs.

For more information about SmartNIC use cases, see the following resources:

• The Next Platform article: Living in the SmartNIC Future
• SDxCentral article: Smart NICs in White Boxes

Learn how to simplify AI model deployment at the edge with NVIDIA Triton Inference Server on NVIDIA Jetson. Triton Inference Server is available on Jetson starting with the JetPack 4.6 release.

AI machine learning (ML) and deep learning (DL) are becoming effective tools for solving diverse computing problems in various fields including robotics, retail, healthcare, industrial, and so on. The need for low latency, real-time responsiveness, and privacy has moved running AI applications right at the edge.

However, deploying AI models in applications and services at the edge can be challenging for infrastructure and operations teams. Factors like diverse frameworks, end to end latency requirements, and lack of standardized implementations can make AI deployments challenging. In this post, we explore how to navigate these challenges and deploy AI models in production at the edge.

Here are the most common challenges of deploying models for inference:

• Multiple model frameworks: Data scientists and researchers use different AI and deep learning frameworks like TensorFlow, PyTorch, TensorRT, ONNX Runtime, or just plain Python to build models. Each of these frameworks requires an execution backend to run the model in production. Managing multiple framework backends at the same time can be costly and lead to scalability and maintenance issues.
• Different inference query types: Inference serving at the edge requires handling multiple simultaneous queries, queries of different types like real-time online predictions, streaming data, and a complex pipeline of multiple models. Each of these requires special processing for inference.
• Constantly evolving models:  With this ever-changing world, AI models are continuously retrained and updated based on new data and new algorithms. Models in production must be updated continuously without restarting the device. A typical AI application uses many different models. It compounds the scale of the problem to update the models in the field.

NVIDIA Triton Inference Server is an open-source inference serving software that simplifies inference serving by addressing these complexities. NVIDIA Triton provides a single standardized inference platform that can support running inference on multiframework models and in different deployment environments such as datacenter, cloud, embedded devices, and virtualized environments. It supports different types of inference queries through advanced batching and scheduling algorithms and supports live model updates. NVIDIA Triton is also designed to increase inference performance by maximizing hardware utilization through concurrent model execution and dynamic batching.

We brought Triton Inference Server to Jetson with NVIDIA JetPack 4.6, released in August 2021. With NVIDIA Triton, AI deployment can now be standardized across cloud, data center, and edge.

Key features

Here are some key features of NVIDIA Triton that help you simplify your model deployment in Jetson.

Embedded application integration

Direct C-API integration is supported for communication between client applications and Triton Inference Server, though gRPC and HTTP/REST are supported. On Jetson, where both the client application and inference serving runs on the same device, client applications can call Triton Inference Server APIs directly with zero communication overhead. NVIDIA Triton is available as a shared library with a C API that enables the full functionality to be included directly in an application. This is best suited for Jetson-based, embedded applications.

Multiple framework support

NVIDIA Triton has natively integrated popular framework backends. Models developed in TensorFlow or ONNX or optimized with TRT can be run directly on Jetson without going through a conversion. NVIDIA Triton also supports flexibility to add custom backend. The developers get their choice and the infrastructure team streamlines the deployment with a single inference engine.

DLA support

Triton Inference Server on Jetson can run models on both GPU and DLA. DLA is the Deep Learning Accelerator available on Jetson Xavier NX and Jetson AGX Xavier.

Concurrent model execution

Triton Inference Server maximizes performance and reduces end-to-end latency by running multiple models concurrently on Jetson. These models can be all the same models, or different models from different frameworks. The GPU memory size is the only limitation to the number of models that can run concurrently.

Dynamic batching

Batching is a technique to improve inference throughput. There are two ways to batch inference requests: client and server batching. NVIDIA Triton implements server batching by combining individual inference requests together to improve inference throughput. It is dynamic because it builds a batch until a configurable latency threshold. When the threshold is met, NVIDIA Triton schedules the current batch for execution. The scheduling and batching decisions are transparent to the client requesting inference and is configured per model. Through dynamic batching, NVIDIA Triton maximizes throughput while meeting the strict latency requirements.

One of the examples of dynamic batching is where your application involves running both detection and classification models, where the input to classification model are the objects detected from the detection model. In this scenario, since there can be any number of detections to be classified, dynamic batching can make sure that the batch of detected objects can be created dynamically and classification can be run as a batched request, reducing the overall latency and improving the performance of your application.

Model ensembles

The model ensemble feature is used to create a pipeline of different models and pre– or post-processing operations to handle a variety of workloads. NVIDIA Triton ensembles represent a pipeline of one or more models and the connection of input and output tensors between those models. NVIDIA Triton can easily manage the execution of the entire pipeline just with a single inference request to an ensemble from the client application. As an example, applications trying to classify vehicles can use NVIDIA Triton model ensembles to run a vehicle detection model and then run vehicle classification model on the detected vehicles.

Custom backends

In addition to the popular AI backends, NVIDIA Triton also supports execution of custom C++ backends. These are useful to create special logic like pre– and post-processing or even regular models.

Dynamic model loading

NVIDIA Triton has a model control API that can be used to load and unload models dynamically. This enables the device to use the models when required by the application. Also, when a model gets retrained with new data it can be deployed by NVIDIA Triton for inference seamlessly without any application restarts or disruption to the service.

Conclusion

Triton Inference Server is released as a shared library for Jetson. NVIDIA Triton releases are made monthly, which adds new features and supports newest framework backends. For more information, see Triton Inference Server Support for Jetson and JetPack.

NVIDIA Triton helps with a standardized scalable production AI in every data center, cloud, and embedded device. It supports multiple frameworks, runs models on multiple computing engines like GPU and DLA, handles different types of inference queries. With the integration in NVIDIA JetPack, NVIDIA Triton can be used for embedded applications.

For more information, see the triton-inference-server Jetson GitHub repo for documentation and attend the upcoming webinar, Simplify model deployment and maximize AI inference performance with NVIDIA Triton Inference Server on Jetson. The webinar will include demos on Jetson to showcase various NVIDIA Triton features.