Embracing Edge AI can be a game-changer for businesses, propelling them toward a future where real-time data processing and decision-making are at the forefront of innovation. Edge AI is a term that refers to the deployment of artificial intelligence (AI) models closer to users and devices, either on-premises — such as in a retail store or bank branch — or on edge computing platforms. With this approach, AI processing occurs near where data is created, on the “edge” of the network, in a decentralized manner, instead of using cloud-based solutions or centralized computing. This results in reduced latency, improved performance, and enhanced privacy and security.

Many industries, including retail, finance, manufacturing, healthcare, automotive, and telecommunications, are investing in Edge AI to increase efficiency in their operations — essentially by providing a large set of automation possibilities — and to improve their customer experiences. Demand for this technology is growing, and the overall state of Edge AI adoption is gaining traction.

According to Gartner, “More than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021.” And according to IDC, by “2023 more than 70% of organizations will run varying levels of data processing at the IoT edge.”

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The Benefits of an Edge AI Approach

One of the main benefits of Edge AI over running AI in centralized computing is improved efficiency. By processing data locally rather than sending it to a central server, Edge AI can drastically reduce latency and remove bandwidth constraints, leading to faster decision-making and improved operational efficiency.

This allows for real-time or near-real-time processing of data, a critical feature in many applications, such as autonomous vehicles, manufacturing processes, and healthcare monitoring. This type of processing is also a key advantage when the goal is to improve the customer experience.

Businesses also save on bandwidth and data storage costs by minimizing the need to transmit vast amounts of raw data back to the cloud or a data center; instead, only the relevant preprocessed information is sent.

This is much more scalable and sometimes can enable solutions that otherwise wouldn’t be technically or economically feasible, and it also brings increased resiliency: Edge AI systems can continue to process data even when they lose connectivity, enhancing the reliability of weak communication links and optimizing costs.

Last, but not least, the Edge AI approach can offer enhanced privacy and security: since data is processed locally, Edge AI reduces the risk of data breaches or loss during transmission. This makes it suitable for industries with stringent privacy regulations because sensitive data does not need to leave the premises.

Edge AI in Action

It has been said that “data is the new oil,” which means that any connected source of information is also a juicy target for criminals. AI is increasingly important in these scenarios as the attacks become more advanced.

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Edge AI security measures, like access control, can be delegated to edge nodes, which can run sophisticated AI-based algorithms for inference and behavioral analysis that can detect and stop suspicious activity from cyberattacks — including the feared “zero-day” attacks — before they even enter business networks and cause any harm.

In retail, Edge AI can enable smarter customer experiences, such as personalized recommendations based on real-time in-store behavior, including monitoring self-checkouts to reduce losses or running real-time inventory management. In manufacturing, it can assist in predictive maintenance by processing data from numerous sensors to predict equipment failures and suggest timely maintenance, minimizing downtime.

In healthcare, patient monitoring devices can use Edge AI to process health data in real-time, alert healthcare providers of any immediate risks, and ensure patient privacy by keeping sensitive health data localized. Security systems equipped with Edge AI can process video footage in real-time for facial recognition, anomaly detection, or immediate threat analysis and alerting.

Telecom operators can use Edge AI to optimize network operations by analyzing network traffic in real-time and dynamically managing network resources. Similarly, Edge AI can be used for managing smart power grids, detecting anomalies, and predicting energy demand to optimize power generation and distribution.

Steps for Preparing to Adopt Edge AI

Organizations looking to adopt Edge AI will first need to identify the specific use cases or areas in their business where Edge AI could provide a considerable benefit. This should be followed by an evaluation of the technical requirements since Edge AI involves different technology than traditional cloud-based AI.

The next step is to ensure your team has the necessary skill set or that your organization has a mechanism in place for training. Teams will need to be educated on edge computing, AI model development, and edge node orchestration.

At this stage, a partnership with edge computing providers and companies that provide ready-to-use AI models is all it takes to start collecting results, as your company will be able to leverage an integrated solution and vendor expertise.

Edge AI aligns with strategic growth initiatives, seamlessly integrates with change management practices, and may even catalyze the emergence of groundbreaking business models. The adoption of Edge AI signifies a commitment to staying ahead of the curve, ensuring that your enterprise remains competitive in a rapidly evolving digital landscape.

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Driver•i One brings customers business-critical, comprehensive video safety and key Geotab compliance features in an integrated best-of-breed solution

Netradyne, Inc., an industry-leading SaaS provider of artificial intelligence (AI) and edge computing solutions focused on safety and driver coaching for commercial fleets, announced the launch of its Driver•i One solution, making the company a full telematics solution and ELD provider. Netradyne’s Driver•i One fleet safety solution is a robust, professional grade video telematics device that can deliver data to power many of the solutions that fleets need. By combining Netradyne’s unique approach to driver-centric, AI-powered safety with best-in-class fleet management and compliance using Geotab’s proven analytics platform and UI, Netradyne has achieved a powerful, seamless, single-pane-of-glass user experience that rivals the best all-in-one safety telematics solutions available. This consolidation of safety and fleet management not only means an integrated view but streamlined subscription billing, one piece of hardware, and lower costs – all with hands-on customer support from Netradyne.

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Driver•i One How it Works Highlights:

• Safety: Netradyne’s best-in-class Driver•i AI-powered fleet camera system is paving the way for the future of safe mobility with automated, AI-powered coaching, risk identification, and exoneration. Safety managers are in a better position to retain the best drivers with Driver•i, the only fleet camera that can identify, reinforce, and reward great driving behavior. Conversely, safety managers can better assess key risks like distracted driving behavior and performance trends to educate accordingly, all while protecting the bottom line and reducing fleet risk.
• Fleet Tracking: The Driver•i One solution offers fleet tracking capabilities for enhanced visibility and optimization across fleets with visibility into trips and activity. Near real-time GPS tracking allows customers to select areas on the map to create zones that trigger notifications when a vehicle enters and leaves the location that can then be shared with third parties like customers or vendors. An advanced rules engine can be leveraged to manage and automate workflows. Routing reports gleaned from fleet tracking can be used to coach drivers to be more efficient and trip history reviews can provide insight into distance traveled, idling time and stop duration within the past six months.
• Fleet Management: Amid rising fuel and maintenance costs, road hazards and customer demand for faster delivery, fleets are under pressure to improve performance. Netradyne’s integrated fleet safety and management solution combines its Driver•i intelligent dashcam and video telematics platform with several industry-leading fleet management features to help increase productivity and reduce costs. Driver•i’s near real-time collection of vehicle data allows for fuel usage tracking, identifying, and addressing maintenance issues before they impact performance, and improved route optimization, among other benefits.
• Compliance: Complying with Hours of Service (HOS), Driver Vehicle Inspection Report (DVIR), and International Fuel Tax Agreement (IFTA) mandates is complex and challenging. Powered by Driver•i One and supported by Geotab’s compliance features, fleets can avoid violations and fines, reduce paperwork while increasing accuracy, ease drivers’ ability to manage HOS and DVIRs and streamline IFTA mandates with simple mileage report generation. The Driver•i device is Federal Motor Carrier Safety Administration (FMCSA) certified, so there’s no need for separate Electronic Logging Device (ELD) hardware in the cab. With one device, one view, and one vendor to deliver it all, customers can increase safety and operational efficiency for their fleet.

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“Netradyne was created to provide one of the most vital industries with an unwavering and unique approach to road safety. We’ve successfully done that and only continue to improve and innovate with the latest technologies available,” said Barrett Young, CMO of Netradyne. “In introducing our Driver•i One solution, our top-of-line AI-powered safety now extends into fleet management and compliance – addressing the evolving needs of commercial fleets of all sizes with one device and one view. As our biggest launch since starting the company with our groundbreaking solution Driver-i, we are thrilled to offer our customers and the greater industry a fundamentally different way to streamline and optimize their fleet management technology during a time when efficiency and cost savings could not be more important.”

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“Being an industry leader in fleet safety is of the utmost importance across our entire company,” said Stephenie Barnes, Director of Fleet Operations at G & J Pepsi-Cola Bottlers, Inc. “Putting safety first while enhancing fleet management with better operational efficiency and fuel maintenance, we can not only improve our bottom line but can continue to do our part in keeping our drivers and all drivers on the roads safe.”

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Elisa Oyj, a leading telecommunications company, has achieved a significant milestone in the field of edge computing with the successful deployment of its first fully automated edge data center site, which is the second edge site to go into commercial production this year. This groundbreaking project combines Wind River Studio Cloud Platform as the production-grade distributed Kubernetes solution for managing cloud infrastructure, User Plane Function (UPF) application from Elisa’s current 5G core vendor, and the advanced capabilities of Wind River Studio Conductor, an industry leading platform that delivers a single pane of glass to manage and automate applications deployment in large-scale distributed environments.

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“As a leader in the 5G landscape that powers the majority of 5G vRAN/O-RAN deployments with global operators, our Wind River Studio capabilities address service providers’ complex challenge of deploying and managing a physically distributed, ultra-low latency cloud-native infrastructure”

The deployment of this state-of-the-art edge data center site represents a significant achievement to strengthen Elisa’s position as the leading digital service provider in automating operations. It showcases the company’s commitment to embracing next-generation technologies to unlock the potential of edge computing for a wide range of industries, including telecommunications, transportation, healthcare, and manufacturing.

”We are delighted to continue our efficient collaboration with Wind River and take the first Fully Automated Edge Data Center into commercial service. Constant automation development is our key to future success. Combined with Wind River´s distributed cloud capabilities, we are able to further improve our customer satisfaction by reducing the time to deploy and adding the quality of the process. This strengthens Elisa’s position as the leading digital service provider in automating operations and edge deployments,” says Markus Kinnunen, Vice President, Cloud Services, Elisa.

“As a leader in the 5G landscape that powers the majority of 5G vRAN/O-RAN deployments with global operators, our Wind River Studio capabilities address service providers’ complex challenge of deploying and managing a physically distributed, ultra-low latency cloud-native infrastructure,” said Avijit Sinha, Chief Product Officer, Wind River. “Elisa is an innovator, and with their recent milestone, they are helping to advance the industry.”

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Key to this achievement is the collaboration between the industry leaders. Wind River Studio provides a fully cloud-native, Kubernetes- and container-based architecture, based on open source, for the development, deployment, operations, and servicing of distributed edge networks at scale. Elisa’s 5G core UPF application enhances network performance, ensuring efficient and reliable data transmission and processing at the edge. Leveraging the advanced capabilities of Wind River Studio Conductor, the deployment process becomes fully automated, enabling rapid and efficient provisioning of edge data center resources.

The major outcomes can be grouped in three areas: time to prepare, time to deploy and quality of the process:

• Time to prepare: The biggest savings came from parameter settings followed by environment preparation work. The team was able to execute hundreds of tests and scripts before going into live network in the same amount of time that it would typically take to manually run much fewer tests in the traditional model.
• Time to deploy: The overall Operator time, measured in terms of staff hours, for commissioning the edge DC site from preparation, parameter settings to complete installation and testing is reduced by 90% as compared to doing the very same tasks manually. Furthermore, the overall deployment time is reduced by around 50% thanks to the automation of the processes.
• Quality of the process: Finally, automation brings in additional benefits where multiple tasks can now be run in parallel with minimal probability of human errors, reducing time to service and enhancing network quality.

The successful implementation of this milestone project paves the way for future advancements in edge computing by Elisa. It sets a precedent for the seamless integration of Studio Cloud Platform, critical applications, and automation technologies, empowering industries to accelerate digital transformation and unlock new possibilities.

The collaborative effort involved in this achievement underscores the commitment of Elisa and its technology partner Wind River to drive innovation and push the boundaries of what is possible in the world of edge computing. Elisa continues to position itself as a leader in cutting-edge technologies, driving economic growth and benefits to the society.

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CyberLink Corp. a pioneer in AI and facial recognition technologies, announces FaceMe Security is now deployed on Axis Camera Application Platform (ACAP) on Axis ARTPEC-8 cameras. FaceMe Security is a facial recognition software solution that allows businesses to painlessly upgrade existing smart security and access control infrastructure. Compatible with most surveillance video management systems and Axis IP cameras, FaceMe Security provides identity authentication, attendance management, access control, security alerts, and AI smart monitoring.

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“Thanks to the Axis open platform and standardized VMS integration protocols, you can work with your customers’ existing VMS solutions while simultaneously integrating Axis devices with other IT applications, like FaceMe Security”

Axis is a network technology company and industry leader that offers solutions in video surveillance, access control, intercoms, and audio systems. The Axis Camera Application Platform (ACAP) enables a wide portfolio of computer vision and video analytics to be added to their products. “Thanks to the Axis open platform and standardized VMS integration protocols, you can work with your customers’ existing VMS solutions while simultaneously integrating Axis devices with other IT applications, like FaceMe Security,” said Hiroshi Ochiai, Director of Alliances, Portfolio and Customer Services, Axis, Asia Pacific.

FaceMe Security processes video streams for face recognition and face template extraction. This facial data is then searched and compared to ensure accurate verification for access control, video analytics inclusive of data group management (e.g., VIP, blocklists, employees), and alert notifications. As an Axis Technology Integration Partner supporting ACAP, FaceMe’s facial recognition can now be done at the edge by running directly on Axis cameras.

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ACAP enables a reduced workload on the facial recognition engine, improving the overall performance of FaceMe Security. Additionally, when deployed with ACAP cameras, FaceMe Security can support even more video channels, all while users reap the benefits of enhancing their surveillance system with FaceMe Security. They can layer person-of-interest detection, group tagging and management, and visitor summaries on top of existing IP surveillance configurations with Axis cameras and VMS.

“AI facial recognition requires a great deal of computing power, and leveraging Axis’ open application platform to add these functions at the edge means an enhanced experience for our customers,” said Dr. Jau Huang, CEO of CyberLink. “We are proud to be an Axis Technology Integration partner, and ACAP is a proven platform that adds to the performance of FaceMe Security.”

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As oil and gas prices remain volatile, companies are looking to companies like ZEDEDA, the leader in edge infrastructure orchestration, to leverage the power of data and increased automation to optimize operations and gain a competitive advantage.

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“Deploying edge computing platforms can help organizations become more efficient, keep people safe and achieve a greener future.”

Pulling timely actionable insights from edge environments like oil rigs, wells and refineries can save millions of dollars by reducing equipment failure and safety issues and maintaining regulatory compliance. These environments, however, can be very remote and may have limited on-site staff, so addressing these challenges requires tools designed to simplify managing and securing edge infrastructure.

ZEDEDA has been a key partner in helping innovative oil and gas operators accelerate their digital transformation and increase their ability to remotely manage connected operations. Working with Advantech and Arrow Electronics, the company produced a new, combined solution for Connected Oilfield Systems to help streamline and reduce costs in oil and gas field operations.

Even though executives recognize digitization as the key to unlocking the gains they seek, deploying those technologies at scale remains elusive. Research from McKinsey showed that while almost every company has begun running digitization projects, 70% have not moved beyond the pilot phase.

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ZEDEDA Joins Innovation Panel at Data Driven Oil & Gas USA 2023 Conference

ZEDEDA’s VP of Business Development, Michael Maxey, will present at the Data Driven Oil & Gas USA 2023 Conference on Tuesday, June 20 at 2:50 pm CT. Maxey will be part of the “Cutting Edge — Latest Innovative Technology” panel, along with Jim Sage, IT Manager – Emerging Technologies & Solution Architecture at Oxy; Scott Parent, VP Field Technology at ANSYS; Prabu Parthasarathy, Global VP Partner Success at Cognite and Joe Perino, President & CEO at PERTEX.

The discussion will look at how technological advances are making data-driven operating models a reality for oil and gas companies as they look to digitize their businesses. Because their equipment is often in remote, unstaffed locations, companies need real-time visibility into their assets and data to operate effectively.

“There’s so much opportunity for the oil and gas industry to innovate with edge AI and IoT solutions,” said Maxey. “Deploying edge computing platforms can help organizations become more efficient, keep people safe and achieve a greener future.”

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StackPath, an industry-leading edge computing platform that provides both infrastructure as a service and applications, announced a formal strategic partnership with Bridgepointe Technologies, a tech advisory firm that helps mid-market and enterprise businesses bridge the gap between tech investments and business results. StackPath edge compute offerings—including virtual machines (VMs) and containers, with L3-L4 DDoS attack protection—will be added to the Bridgepointe Technologies hybrid cloud portfolio so businesses can deploy edge compute instances in seconds, for unmatched scalability, performance, and time-to-solution.

“StackPath is an excellent alternative to bare metal applications, and we’re looking forward to adding our unique Edge Compute offerings to Bridgepointe Technologies’ partner ecosystem portfolio,” said Frank Stepczyk, StackPath’s Head of Channel & Partnerships. “With our focus on making it simple and cost-effective to deploy decentralized workloads, unmatched scalability, and our global presence, StackPath is the leading edge-computing platform today.”

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StackPath edge locations are closer to end users and connected through a private dedicated network, enabling data to reach its Edge Compute instances up to 2.6x faster than cloud computing instances from centralized cloud providers. StackPath Edge Compute, Delivery, and Security are delivered in one, seamlessly integrated platform, providing tangible benefits that translate into real business value to Bridgepointe Technologies customers.

“The partnership with StackPath is a key supplier addition in our Hybrid Cloud Portfolio, providing our customers additional choice for Edge Computing including Virtual Machines, Containers, and L3-L4 DDoS offerings,” said Mel Melara, VP of Sales & Head of the Data Center Practice, Bridgepointe. “They’ve had success in key verticals such as Media and Entertainment, Gaming, Web3, SaaS, and others. Their highly attentive support and knowledgeable engineering teams have been impressive, and we’re excited to officially include StackPath as one our suppliers.”

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NTT Research, a division of NTT, announced that a team of scientists associated with its Physics and Informatics (PHI) Lab, the Massachusetts Institute of Technology (MIT) and several optical computing companies have demonstrated an approach to optically driven deep neural networks (DNN), dubbed Netcast, which resolves the memory-access bottleneck in resource-constrained edge devices, enabling orders of magnitude energy and latency reduction. Conceived by PHI Lab Senior Scientist Ryan Hamerly, Netcast was demonstrated in work performed by MIT Ph.D. candidate Alexander Sludds, under the joint supervision of Dr. Hamerly and MIT Professor Dirk Englund, and with the help of a wide group of collaborators. Their research was summarized in an paper titled “Delocalized photonic deep learning on the internet’s edge,” and published in the October 20 issue of Science, one of the world’s top academic journals.

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News: @NttResearch’s Dr. Ryan Hamerly, @MIT Professor @Dirk_Englund and other collaborators are resolving critical bottlenecks in computationally intensive tasks on #computing devices by using optically accelerated #edgecomputing. #UpgradeReality

Deep neural networks, now pervasive in science and engineering, feature several layers of interconnected neurons or nodes hidden between input and output layers. Once trained and calibrated with appropriate weights, DNN models can enable rapid classification of data and execution of other computationally intensive tasks, such as image or speech recognition. A critical bottleneck in such tasks on present-day computing devices relates to matrix algebra. Despite the offloading of DNN inference to cloud servers and the development of non-digital technologies, including optical computing, memory access and multiply-accumulate (MAC) functions have remained points of congestion. Offloading also adds security risks because data from the edge must be shared with the cloud. As the lead author in a 2019 paper on optical neural networks and photoelectric multiplication, Dr. Hamerly proposed addressing those challenges by encoding the DNN model in an optical signal and streaming it into an edge processor. Expanding on that proposal, the Science article introduces Netcast and its components: a smart transceiver that could be integrated into cloud computing infrastructure and a time-integrating optical receiver that resides in the client. The article explains how the scheme achieved up to 98.8% accuracy in image recognition, cut server-side latency by performing actual computation at the client and vastly outperformed the status quo in energy consumption.

“The difference is that a GPU high-bandwidth memory link consumes around 100 watts and only connects to a single GPU, whereas a Netcast link can consume milliwatts, and by using trivial optical fan-out, one server can deploy a DNN model to many edge clients simultaneously,” Dr. Hamerly said. “This broadcasting of DNNs is one reason why we chose the term Netcast.”

Once the idea of a single physicist, Netcast now has a wide range of contributors who have helped bring it to reality. The Science article’s sixteen co-authors are affiliated with NTT Research; with MIT’s Research Laboratory of Electronics, MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and MIT’s Lincoln Laboratory; and with the optical computing companies Elenion (purchased by Nokia), Luminous and Lightmatter. The project has enjoyed support from NTT Research, which announced a five-year joint research agreement with MIT in November 2019 that included the goal of developing photonic accelerators for deep learning. Corresponding author Alexander Sludds discussed Netcast at Coherent Network Computing (CNC) 2022, an NTT Research-organized conference that took place October 24–26 at Stanford University.

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“We are delighted to see Dr. Hamerly’s first Netcast paper and congratulate him, Professor Englund and the entire team that supported the experimental demonstration of this innovative application of optical computing to deep learning,” PHI Lab Director Yoshihisa Yamamoto said. “As non-digital computing architectures continue to emerge, we anticipate more explorations of how best to amplify their benefits.”

To demonstrate Netcast experimentally, researchers at MIT connected the smart transceiver to the client receiver over 86 km of deployed optical fiber in the Boston area. The test itself involved a digital classification task. Using a three-layer model with 100 neurons per hidden layer and 1,000 handwritten images drawn randomly from the MNIST dataset (digits 1–10), the test yielded results comparable with the model’s baseline accuracy of 98.7% when used locally, and 98.8% when using 3 THz of bandwidth over fiber. At the Netcast client, optical energy consumption has been demonstrated as scalable to less than 1 photon per MAC, a fundamental quantum limit highly relevant in photon-starved scenarios. Overall client-side energy consumption can plummet three orders of magnitude below what is possible in existing digital semiconductors. It is possible, but not proven and an open question left for future work, that the laws of quantum mechanics could secure from eavesdropping a DNN model’s weights, which can be costly to derive. Because the hardware used in this demonstration of Netcast is readily available, the authors indicate that it could potentially deliver a near-term impact, although which applications to target also remains an open question.

The PHI Lab has broadly advocated for new approaches to computing, such as the Coherent Ising Machine (CIM), an information processing platform based on photonic oscillator networks. The scope of CNC 2022, which drew 40 speakers and more than 30 poster presenters, encompassed principles and technologies related to “novel computing machines that utilize analog physical dynamics.” In addition to the joint research with MIT, the PHI Lab has similar arrangements with ten other universities. These include the California Institute of Technology (CalTech), Cornell, Harvard, Michigan, Notre Dame, Stanford, Swinburne University of Technology, the Tokyo Institute of Technology, the University of Michigan and the University of Tokyo. The NASA Ames Research Center in Silicon Valley and 1QBit, a private quantum computing software company, have also entered joint research agreements with the PHI Lab. In March 2022, NTT Research joined the MIT AI Hardware program as an inaugural industrial member.

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