https://medium.com/google-developers/computing-at-the-edge-of-iot-140a888007b
We’ve seen that demand for low latency, offline access, and enhanced machine learning capabilities is fueling a move towards decentralization with more powerful computing devices at the edge.
Nevertheless, many distributed applications benefit more from a centralized architecture and the lowest cost hardware powered by MCUs.
Let’s examine how hardware choice and use case requirements factor into different IoT system architectures.
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Tomi Engdahl says:
New Architectures Bringing AI to the Edge
https://www.eetimes.com/document.asp?doc_id=1333920
As artificial intelligence (AI) capability moves from the cloud to edge, it is inevitable that chipmakers will find ways to implement AI functions like neural-network processing and voice recognition in smaller, more efficient, and cost-effective devices.
The big, expensive AI accelerators that perform tasks back in the data center aren’t going to cut it for edge node devices. Battle lines are being drawn among various devices — including CPUs, GPUs, FPGAs, DSPs, and even microcontrollers — to implement AI at the edge with the required footprint, price point, and power efficiency for given applications.
To that end, a pair of intriguing architectures created specifically for implementing AI at the edge are being introduced at the Linley Processor Conference on Tuesday by Cadence Design Systems and Flex Logix Technologies. Both focus on bringing AI functionality into edge node devices with an emphasis on reducing the memory footprint.
Tomi Engdahl says:
Startup Shifts Cloud Services to IoT
Mimik drives microservices to end nodes
https://www.eetimes.com/document.asp?doc_id=1333940
Startup Mimik released software that enables the equivalent of cloud services to run on devices including end nodes in the internet of things (IoT). The so-called edgeSDK aims to lower response times and open up new use cases.
The code lets any device running a popular operating system host the kind of microservices usually managed by the likes of AWS or Microsoft Azure. Early users include companies in gaming and health care, as well as Lime Microsystems, that will bundle the SDK with its open-source base stations.
Carriers in the Facebook-led Telecom Infra Project aim to use the software “so they don’t have to go to a cloud service for communications in villages in Africa where connecting to a remote data center over an expensive satellite link would not be viable,” said Fay Arjomandi, Mimik’s co-founder and chief product officer.
Facebook Likes $1K Base Stations
Carriers to test open source hardware in 2018
https://www.eetimes.com/document.asp?doc_id=1332576
Tomi Engdahl says:
AI Edges to Factory Floor
https://www.eetimes.com/document.asp?doc_id=1333973
Deep neural networks are crawling toward the factory floor.
For several early adopters, neural nets are the new intelligence embedded behind the eyes of computer-vision cameras. Ultimately, the networks will snake their way into robotic arms, sensor gateways, and controllers, transforming industrial automation. But the change is coming slowly.
“We’re still in the early phases of what’s likely to be a multi-decade era of advances and next-generation machine learning algorithms, but I think we’ll see enormous progress in the next few years,” said Rob High, chief technology officer for IBM Watson.
Tomi Engdahl says:
APAC firms look to edge for faster response but worry over data security
https://www.zdnet.com/article/apac-firms-look-to-edge-for-faster-response-but-worry-over-data-security/
Edge computing is being sought out for faster response and cost savings, but there are concerns about security and latency when large volumes of data are processed on such platforms.
Organisations in Asia-Pacific are seeking out edge computing in search of faster response and cost savings, but they also have concerns about security and latency when large volumes of data are processed on such platforms.
A primary, and often cited, benefit of edge deployments are the rapid response times that would not be possible if data is sent back to a centralised network for processing.
Taiwan’s Taoyuan City, for instance, turned to edge technology in rolling out smart streetlights in its Qingpu district, using HPE’s Edgeline EL10 Internet of Things (IoT) Gateway.
The Taiwanese city has ambitions of becoming a smart city and is looking to deploy and integrate multi-sensor information from edge products into a centralised platform to deliver better citizen services.
“Certain citizen intelligence applications and services require an almost immediate response time [and] this cannot be achieved if data needs to be transmitted back to a centralised cloud for processing,” a spokesperson for Taoyuan City Government’s Public Works Department told ZDNet.
To address customer concerns about outdoor or physical attributes, vendors such as HPE have designed their products to withstand various external factors such as dirt, humidity, temperatures, and vibration.
When asked about the initial concerns that the Taoyuan government may experience when deploying the edge technology, the spokesperson pointed to the need to closely monitor such systems.
“Intelligent edge solutions typically require massive data processing and network connectivity. Hence, ensuring regular system updates as well as stability of the various decentralised devices is critical,” she said.
“Furthermore, as citizens increasingly rely more on such services, we need to ensure the data collected from multiple sensor devices is stored properly and securely.”
Tan noted that HPE’s edge systems supported unmodified enterprise software from its partner community, including Citrix, SAP, GE Digital, and Microsoft. This meant that enterprise customers could use the same application stacks at the edge, in datacentres, as well as cloud.
Key considerations before going to the edge
Taoyuan City’s streetlight management edge deployment is still currently in its pilot phase and the government has plans to deploy more streetlights over the next few phrases of the project, according to the spokesperson.
She noted that the city government is hoping to introduce more innovative services by analysing the data collected in the deployment, spanning parameters such as air quality, climate indicators, and image analysis processing.
In deciding the volume and type of data that should and should not be analysed at the edge, she said the Taoyuan government assessed the network transmission bandwidth of the field device as well as the data management centre.
It also considered the immediacy of the application service, whether it required real-time processing and feedback, and whether edge computing could support the required speed and security, she noted.
She added that, compared to traditional datacentres, outdoor environments are harsher and edge deployments in such situations would need to consider factors such as weather, dust conditions, temperature as well as stability of power supply to the device.
Tomi Engdahl says:
Cadence’s Paul McLellan shares highlights from five presentations all discussing what’s behind AI’s movement to edge devices, the vast amount of investment going into the area, and where a few of the foreseeable challenges lie.
Bagels and Brains: SEMI’s Artificial Intelligence Breakfast
https://community.cadence.com/cadence_blogs_8/b/breakfast-bytes/posts/semi18-ai
Tomi Engdahl says:
5 Reasons Why Azure IoT Edge Is Industry’s Most Promising Edge Computing Platform
https://www.forbes.com/sites/janakirammsv/2018/07/01/5-reasons-why-azure-iot-edge-is-industrys-most-promising-edge-computing-platform/#5d7f16cb3249
Out of the top 5 public cloud platforms – AWS, Azure, Google Cloud Platform, IBM Cloud and Alibaba Cloud – only Microsoft and Amazon have a sophisticated edge computing strategy. Other players are yet to figure out their story for edge computing.
Amazon’s edge platform is delivered through AWS Greengrass – a service that was announced at re:Invent event in 2016 and became generally available in June 2017. AWS recently added the ability to perform inferencing of machine learning models. It also started bundling AWS Greengrass in devices such as AWS DeepLens, a smart camera that can run neural nets at the edge.
Microsoft shipped Azure IoT Edge almost after a year of AWS Greengrass’ general availability. However, the wait has been absolutely worthwhile. Firstly, the market dynamics have evolved in the last year giving the team an opportunity to align with customer scenarios. Secondly, Microsoft got a chance to improvise its platform to make it better than the only other offering – AWS Greengrass.
1. Open sourcing the platform
2. Containers at the core
3. Ecosystem engagement
4. Security
5. AI @ Edge
Azure IoT Edge plays a crucial role in Microsoft’s vision of delivering Intelligent Cloud and Intelligent Edge. Some of the design decisions such as containerized modules, tight integration with HSM, plugins for Visual Studio turn Azure IoT Edge into one of the most comprehensive edge computing platforms in the industry.
Tomi Engdahl says:
Japan Sniffing Out Its AI Niches
https://www.eetimes.com/document.asp?doc_id=1334011
As with any trade show featuring “embedded technology” anywhere in the world, the Embedded Technology 2018 Exhibition in Yokohama earlier this month got hijacked by today’s two hot topics: AI and IoT.
On one hand, Japanese electronics heavyweights — mostly Fujitsu, NEC and Toshiba — showcased new materials and wireless technologies they deem critical to the spread of IoT applications.
On the other hand, this year’s Embedded Technology/IoT show trotted out a host of Japanese startups, including Ascent Robotics, LeapMind, Robit and others with an intense business and technology focus on AI.
Japanese startups tend to differ from startups elsewhere in their commitment to leverage Japan’s decades of experience in building robots and automobiles. They want to use their proximity to automated manufacturing sites and to experienced factory managers as a head start toward developing AI algorithms for industrial applications.
While Google, Facebook, Amazon and others in the United States may have already established a stronghold in areas like big data, data centers and deep learning, Japan’s hopes focus on making edge devices smarter, more connected and autonomous.
Tomi Engdahl says:
Imec, CEA-Leti Form AI and Quantum Computing Hub
https://www.eetimes.com/document.asp?doc_id=1334000
Two of Europe’s key electronics and nanotechnologies research institutes — imec in Belgium and CEA-Leti in France — will collaborate to develop a European hub for artificial intelligence and quantum computing.
As security and privacy issues rise up the agenda in almost every organization, the race is on to process more at the edge and put more intelligence at endpoints. For electronics systems design, most of the major chip companies now offer or are developing deep learning and edge AI devices or intellectual property. The edge AI devices are often complete computer sub-systems displaying intelligent behavior locally on the hardware devices (chips), analyzing their environment and taking required actions to achieve specific goals.
Edge AI is considered now to hold the promise of solving many societal challenges — from treating diseases that cannot yet be cured today, to minimizing the environmental impact of farming. Decentralization from the cloud to the edge is a key challenge of AI technologies applied to large heterogeneous systems. This requires innovation in the components industry with powerful, energy-guzzling processors.
Tomi Engdahl says:
Advanced algorithms for analytics on the edge
https://cfemedia1.wpengine.com/articles/advanced-algorithms-for-analytics-on-the-edge/
Substantial computing power in modern industrial PCs and cloud bandwidth considerations make the case to analyze machine performance directly on controllers, before the cloud.
The debate between cloud and edge computing strategies remains a point of contention for many control engineers. However, most agree smart factories in an Industrie 4.0 context must efficiently collect, visualize, and analyze data from machines and production lines to enhance equipment performance and production processes. Advanced analytics algorithms allow companies to sift through this mass of information, or Big Data, to identify areas for improvement.
To some, edge computing devices seems to create an unnecessary step when all data can be managed in the cloud with limitless space. Messaging queuing telemetry transport (MQTT) encryption and data security built into the OPC Unified Architecture (OPC UA) specification ensures all data will remain secure while it’s being transferred. When it comes to analytics and data management, however, edge computing presents important advantages to monitor equipment health and maximize production uptime.
Because of the massive amount of data that modern machines can produce, bandwidth can limit cloud computing or push costs outside of a set budget. New analytics software strategies for PC-based controllers allow controls engineers to leverage advanced algorithms locally in addition to data pre-processing and compression. As a result, a key advance in analytical information is the concept to process data on the edge first, which enables individual machines and lines to identify inefficiencies on their own, and make improvements before using the cloud for further analysis across the enterprise.
asset condition monitoring in intralogistics, used 20 sensors at a 1,000 hertz sampling rate and required 11.4 Mbps JSON. This a relevant test since JSON is a common format to send data to the cloud or across the web.
Without compressing or pre-processing mechanisms, an average 7.2 Mbps internet connection cannot stream data from three or more large machines or from a full logistics operation
Edge devices and advanced algorithms
In the past, most programmable logic controllers (PLCs) were capable of controlling repetitive tasks in machines, but possessed the computing prowess of a smart toaster. Industrial PCs (IPCs) feature ample storage and powerhouse processors, with four, or as many as 36 cores. The automation software packages for these IPCs run alongside Windows, and can support third-party applications and can be accessed remotely. PC-based control software can provide advanced algorithms to manage data, such as pre-processing, compression, measurement, and condition monitoring. This does not require a separate, standalone software platform.
More extensive machine vibration evaluations are possible using DIN ISO 10816-3: Mechanical Vibration-Evaluation of machine vibration by measurements on non-rotating parts. To monitor bearing life and other specific components, algorithms are available to add to a PLC program to calculate the envelope spectrum first and then the power spectrum.
Implementing a cloud and edge strategy
Running advanced algorithms on a local edge device reduces cloud bandwidth requirements and offers an efficient strategy for process optimization. However, that does not mean an operation can or should disconnect from the cloud.
To decide what needs to be sent to the cloud and what can be processed or pre-processed locally, make sure to ask the following questions:
What are the goals your operation wants to achieve through data acquisition in this instance?
Which data sets from which machines need to be analyzed in order to achieve these goals?
What types of data insights does the operation need to improve efficiency and profitability?
Local monitoring with edge computing often works most efficiently to improve the operation of individual machines. However, the cloud provides the best platform to compare separate machines, production lines or manufacturing sites against each other. Implementing both allows an operation to maximize its capabilities.
Tomi Engdahl says:
AI Chip Architectures Race To The Edge
https://semiengineering.com/ai-chip-architectures-race-to-the-edge/
Companies battle it out to get artificial intelligence to the edge using various chip architectures as their weapons of choice.
Tomi Engdahl says:
The Importance of Edge Computing in Industrial Transformation
https://www.controleng.com/webcasts/webcast-the-importance-of-edge-computing-in-industrial-transformation/
Based on a recent LNS survey, over 60% of participating companies have now instituted an Industrial Transformation initiative. To support these initiatives, many industrial companies are rethinking operational architectures and taking a hybrid approach to compute infrastructure; with a combination of traditional on-premise, cloud, and edge.
However, many companies aren’t necessarily realizing the anticipated benefits from Industrial Transformation and there is still confusion regarding how to prioritize these investments.
Tomi Engdahl says:
Living On The Wireless Edge With AI And 5G
https://www.forbes.com/sites/forbescommunicationscouncil/2018/09/06/living-on-the-wireless-edge-with-ai-and-5g/#2ee9ce6b6b4f
We are at the cusp of something truly transformational, driven by two much-hyped yet groundbreaking technology mega trends: artificial intelligence (AI) and 5G. Together they make possible things that never existed and seemed utopian not too long ago. A vivid example of this is the rise of self-driving vehicles. Notwithstanding the recent setbacks in the first iteration of self-driving, a fully autonomous self-driving vehicle will be the epitome of AI and 5G technology.
If you apply the traditional AI approach to self-driving, this intelligence will reside in a centralized cloud. The data collected from the vehicle will be hauled to the cloud for processing, and instructions will be sent back to the vehicle. However, when you consider a moving vehicle in which decisions have to be made in split seconds, this approach simply won’t work.
Tomi Engdahl says:
Intelligent Connectivity: the Fusion of 5G, AI and IoT
https://www.gsma.com/iot/news/intelligent-connectivity-5g-ai-iot/
Intelligent connectivity is the combination of high-speed, low-latency 5G networks, cutting-edge artificial intelligence (AI) and the linking of billions of devices through the Internet of Things (IoT). As these three revolutionary technologies combine they will enable transformational new capabilities in transport, entertainment, industry and public services, and much more beyond.
https://www.gsma.com/IC/report/
Tomi Engdahl says:
Vision AI developer kit combines AI and ML to push deep neural network models out to the intelligent edge
https://iot.eetimes.com/vision-ai-developer-kit-combines-ai-and-ml-to-push-deep-neural-network-models-out-to-the-intelligent-edge/
Companies are in the process of digitally transforming their business by using artificial intelligence (AI) and machine learning (ML). Currently this task is only possible once data is collected from internet-connected devices and stored in the cloud.
The technology challenge with this approach is the strong dependency on a consistent connection to the cloud for sending and collecting data. As data volumes approach larger scales and deep learning requires increasingly more complex algorithms, the inevitable bottleneck will limit the quick adoption of AI and ML technologies.
Currently, AI computations are feasible when vast, potentially bordering on infinite, amounts of computing resources are available from cloud resources. This requires an investment in expensive and powerful computational machines running at the edge. This requires continuous power supplies and direct connectivity to all sensor devices.
Tomi Engdahl says:
Giving a flexible edge to the IoT
https://semiengineering.com/giving-a-flexible-edge-to-the-iot/
How flexible sensors will revolutionize electronics as we know them.
As the Internet of Things (IoT) continues to revolutionize our daily lives, the demand for smaller, smarter, and more diverse flexible technology has never been greater. Increasingly complex demands have driven the development of smart sensors to monitor everything from velocity and proximity to pressure, humidity, and more. Future devices will need to interact with the ambient environment by performing intelligent activities such as fingerprint, vein, and odor recognition, with sensors so small and flexible that they can be integrated into almost anything.
Giving a flexible edge to the IoT
https://community.arm.com/arm-research/b/articles/posts/giving-a-flexible-edge-to-the-iot
How can you make ML work on plastic?
The consortium believes that customized processing engines such as neural networks (NNs) are the key to accelerating development of low-cost and customized flexible, integrated smart systems. Customized for a specific application and capable of operating in extremely parallel fashion to achieve high performance, and consume low power, this will be the first time that a flexible smart device has been created to take advantage of machine learning algorithms in hardware.
Tomi Engdahl says:
Smart dust
https://qz.com/emails/quartz-obsession/1494837/
Imagine a world where computers are as small and weightless as dust particles. You wouldn’t be able to see them, but these mini machines would be everywhere: you might even breathe them. A researcher named Kristofer Pister coined the term “smart dust” back in 1997, and it’s closer to becoming a reality than ever.
Smart dust is a sexier name for tiny wireless microelectromechanical sensors
And we mean tiny: they’re only a couple millimeters in size.
1: Ultimate target size in cubic millimeters for Pister’s smart dust motes
2x2x4: Size, in millimeters, of the Michigan Micro Mote, the smallest computer system in the world
500:1: Ratio of the Michigan Micro Mote’s power consumption to that of a human cell
0.3: Size, in cubic millimeters, of the sequel to the Micro Mote
>10: Years smart dust is from mainstream use, according to tech firm Gartner’s “hype cycle”
Tomi Engdahl says:
Renesas Processor Puts Artificial Intelligence at the End Point
https://www.designnews.com/electronics-test/renesas-processor-puts-artificial-intelligence-end-point/97846557759975?ADTRK=UBM&elq_mid=6868&elq_cid=876648
Processor combines high performance with low power, reducing the need for AI computations to be done at the cloud.
A new processor from Renesas Electronics promises to deliver on-board artificial intelligence (AI) to devices such as body cameras and service robots, reducing the need for intensive computations to be sent to the cloud.
The RZ/A2M is said to offer ten times as much image processing performance as its predecessor, the RZ/A1, and reportedly does so at very low power consumption. “We can be in the tens or hundreds of tera-operations per second, and still maintain under 3 W of power consumption,” Mark Rootz, senior marketing director for Renesas’ Industrial Business Unit, told Design News. “And when you get under 3 W, you can get smaller size, you can get rid of fans, and you can embed intelligence in a portable application like a body cam.”
The key to the processor’s capabilities is that it combines a 528-MHz ARM Cortex A9 CPU with a dynamically reconfigurable processor (DRP) module. The DRP is capable of dynamically changing the configuration of its processing circuit from one clock cycle to the next, enabling it to carry out real-time image processing at very low power
Tomi Engdahl says:
Create Intelligence at the Edge
https://www.hackster.io/contests/ultra96
The challenge
What intelligent applications could you create with the power of programmable logic?
There is no question that artificial intelligence and machine learning are hot right now. AI is already making everyday life better and easier with things like autonomous cars and personal assistant robots.
Tomi Engdahl says:
IIC, OpenFog groups to collaborate
Combined organization to focus on cloud computing and Industrial Internet of Things (IIoT) research.
https://www.controleng.com/articles/iic-openfog-groups-to-collaborate/
The Industrial Internet Consortium and the OpenFog Consortium are merging to bring research and best practices promulgation around cloud and fog computing, 5G cellular communications, and artificial intelligence (AI) under one organization. The two groups announced Dec. 18 they would combine their organizations under the Industrial Internet Consortium (IIC) name and would collaborate on a more holistic approach to furthering the use of the Industrial Internet of Things (IIoT).
“The Industrial Internet Consortium, now incorporating OpenFog, will be the single largest organization focused on IIoT, AI, fog and edge computing in the world. Between both of our organizations we have a remarkable global presence with members in more than 30 countries,” said IIC president Bill Hoffman. “This agreement will help accelerate the adoption of the IIoT, fog and edge computing.”
Cloud computing transmits and stores information through data centers to provide greater computing capacity. Fog computing, also known as edge computing, allows for such calculations to be done at servers on a plant’s premises.
Tomi Engdahl says:
IIC, OpenFog groups to collaborate
Combined organization to focus on cloud computing and Industrial Internet of Things (IIoT) research.
https://www.controleng.com/articles/iic-openfog-groups-to-collaborate/
The Industrial Internet Consortium and the OpenFog Consortium are merging to bring research and best practices promulgation around cloud and fog computing, 5G cellular communications, and artificial intelligence (AI) under one organization. The two groups announced Dec. 18 they would combine their organizations under the Industrial Internet Consortium (IIC) name and would collaborate on a more holistic approach to furthering the use of the Industrial Internet of Things (IIoT).
“The Industrial Internet Consortium, now incorporating OpenFog, will be the single largest organization focused on IIoT, AI, fog and edge computing in the world. Between both of our organizations we have a remarkable global presence with members in more than 30 countries,” said IIC president Bill Hoffman. “This agreement will help accelerate the adoption of the IIoT, fog and edge computing.”
THE INDUSTRIAL INTERNET CONSORTIUM AND OPENFOG CONSORTIUM JOIN FORCES
Under the IIC umbrella, the combined membership will accelerate the adoption of the IIoT, fog and edge computing
https://www.iiconsortium.org/press-room/12-18-18.htm
The Industrial Internet Consortium® (IIC™) and the OpenFog Consortium® (OpenFog) today announced that they have agreed in principle to combine the two largest and most influential international consortia in Industrial IoT, fog and edge computing. The move will bring OpenFog members into the IIC organization at a time when their complementary areas of technology are emerging in the mainstream.
The combined memberships will continue to drive the momentum of the Industrial Internet including the development and promotion of industry guidance and best practices for fog and edge computing. The organizations expect the details to be finalized in early 2019.
“This is great news for the industry. Both organizations have been advancing the IIoT, fog and edge computing, and their members represent the best and the brightest in their fields. It makes sense to merge their expertise and work streams to continue providing the IIoT, fog and edge guidance that the industry needs,” said Christian Renaud, Research Vice President, Internet of Things, 451 Research.
Tomi Engdahl says:
European Consortium to Develop Standard Edge Computing Platform
https://www.eetimes.com/document.asp?doc_id=1334148
Eighteen vendors and organizations have signed a cooperation agreement to form a European edge computing consortium, which aims to create a standard reference architecture and technology stack that can be deployed across smart manufacturing, other industrial IoT applications, and network operators.
The Edge Computing Consortium Europe (ECCE) was announced last week with a view to providing a comprehensive edge computing industry cooperation platform. The goals of the initiative include the specification of a reference architecture model for edge computing (ECCE RAMEC), the development of reference technology stacks (ECCE edge nodes), the identification of gaps and recommendation of best practices by evaluating approaches within multiple scenarios (ECCE Pathfinders), and the synchronization with related initiatives/standardization organizations and the promotion of the results.
Members who signed the cooperation agreement are Huawei, Analog Devices, Arm, Bombardier, B&R Automation, Fraunhofer Institute for Open Communication Systems (FOKUS), German Edge Cloud (GEC), German Research Center for Artificial Intelligence (DFKI), HARTING IT, IBM, Intel, KUKA, National Instruments, Renesas Electronics, Schneider Electric, Software AG, Spirent, and TTTech. The agreement stipulates that they will jointly establish the ECCE with the aim of providing a comprehensive edge computing industry cooperation platform for enterprises and organizations in smart manufacturing, operators, and enterprise IoT.
https://ecconsortium.eu/
Tomi Engdahl says:
The advantages of edge computing
https://ces.eetimes.com/the-advantages-of-edge-computing/
With microcontrollers and SoCs becoming more powerful and portable, the demands by customers from even the simplest IoT products is quickly increasing in scale. The burden of this increased analysis and data collection is largely handled by data centers. There is a new solution, referred to as edge computing, that might soon provide some relief.
Tomi Engdahl says:
IoT Merging Into Data-Driven Design
https://semiengineering.com/iot-merging-into-data-driven-design/
Emphasis on processing at the edge adds confusion to the IoT model as the amount of data explodes.
Back in 2013, when the IoT concept really began taking off, connectivity to the Internet was considered the ultimate goal because the biggest compute resources were still in the data center. Today, compute resources are becoming more distributed and processing is becoming more nuanced. In fact, almost all of the early major proponents of the IoT, such as Cisco, Arm, Samsung and Philips, have shifted their IoT focus to data management, processing, and security.
Tomi Engdahl says:
Edge Inferencing Challenges
Balancing different variables to improve performance.
https://semiengineering.com/inferencing-at-the-edge/
Geoff Tate, CEO of Flex Logix, talks about balancing different variables to improve performance and reduce power at the lowest cost possible in order to do inferencing in edge devices.
Tomi Engdahl says:
The Future of IoT Includes Edge Computing, AI, and Blockchain
https://www.designnews.com/automation-motion-control/future-iot-includes-edge-computing-ai-and-blockchain/6234958160013?ADTRK=UBM&elq_mid=7008&elq_cid=876648
IDC forecasts a steep climb in IoT spending through 2022. An IDC researcher explains the stages of IoT development—past, present, and future.
IoT spending is in full bloom, and it’s going to bloom bigger. Research from IDC forecasts that IoT investment for the period 2017 through 2022 will hit $1.2 trillion. That’s an annual growth rate (CAGR) exceeding 13%. IDC’s Worldwide Semiannual Internet of Things Spending Guide forecasts a burgeoning IoT technology market. A good chunk of that total is expected to involve industrial applications. Discrete manufacturing and transportation will each exceed $150 billion in spending in 2022, making these the two largest industries for IoT spending.
Tomi Engdahl says:
Low Power At The Edge
https://semiengineering.com/low-power-at-the-edge/
What’s the real impact of putting a supercomputer in your pocket?
Tomi Engdahl says:
AT&T Preps Edge Cloud Nets
Akraino preps open-source release for April 30
https://www.eetimes.com/document.asp?doc_id=1334217
An open-source group aims to release by April 30 code for carrier edge networks initially driven by AT&T and a team of its vendors. If successful, the Akraino Edge Stack software will someday power “cookie-cutter” deployments of “thousands and tens of thousands … of baby clouds,” said an AT&T executive.
“We’re going to disaggregate the [cellular] radio-access network so it won’t be one big box but many distributed systems using open interfaces,” said Mazin Gilbert, vice president of AT&T Labs, in an interview with EE Times.
The move is “a key ingredient to support our 5G story — to gain speed, minimize latency, deliver security, and minimize dataflow to the core that you need to do more smart processing at the edge,” Gilbert said.
Tomi Engdahl says:
Google and NXP advance artificial intelligence with the Edge TPU
https://www.youtube.com/watch?v=Ou-gulnNkaE
At CES, the Google AIY team shared how it’s advancing AI at the edge with the new Edge TPU chip, integrated with an NXP i.MX8 processor.
Tomi Engdahl says:
Overview of edge computing and MEC
https://www.redhat.com/blog/verticalindustries/overview-of-edge-computing-and-mec/?sc_cid=7016000000127ECAAY
What is “the Edge”?
Mobile Edge Computing (MEC) or Multi-Access Edge Computing, is defined in many ways. In fact, the definition of MEC varies widely by the context you consider it, and sometimes by audience. In this post we will explain the nomenclature and concepts that define telecommunications service providers’ network edge and its use in the delivery of mobile, business and residential services. First let’s take a look at the broader term edge. What is it?
The edge, in the traditional usage, has referred to the point where a “customer connects to the provider.” The provider being the organization providing a service. Largely, this was one of three situations:
An enterprise customer connecting to a service provider’s (SP) edge for network services.
A retail customer connecting to mobility services.
A home user connecting to broadband services.
Tomi Engdahl says:
Internet of Thinking – Creating Intelligent Distributed Systems
https://www.thinkingtech.in/technography/internet-of-thinking/
Internet of Thinking Or IoT Is Being Used A Lot These Days: Let Us Know How?
AI, robotics, immerse reality, connected devices- all of them are undoubtedly huge technological boons but at the same time, they are collectively putting a strain on infrastructure that founded upon. So, as we are becoming more demanding about data we are asking, is it time rethink now and where our data is handled. The internet of thinking is a very curious turn of phrase. It is actually one of the favorite areas that have been talked about in the vision this year, being an interesting term, it also captures what’s happening as intelligent enterprises move forward And talking about the fact that intelligence is something that needs to be embedded in many different parts of the organization, or in many different parts of your operations.
What Internet Of Thinking Actually Means?
The internet of thinking is about how you tie all these different intelligent technology components together in the right way to accomplish what you are doing from a business perspective. If you are in a consumer industry, it might be the device in the consumer’s living room or that they are wearing on the wrist. If you are in the manufacturing company, it might be on your trucks or out in your manufacturing operations.
As we are applying more intelligence, it’s not just all one centralized big-brain system that’s doing this for organizations; it’s intelligence at the edge in many cases, its off-board or off-line intelligence combined together with different, more centralized means.
we see things like IoT, edge IoT, coming back to the device itself.
Impact Of Internet Of Thinking
Amazingly, this internet of things is reducing things by 40- to 44 percent. They are figuring these out by periodically uploading the data back to a database that’s going to take not one individual, but every single person using this and using the signals, the data, they are getting off it in order to refine, to create better algorithms then go down back to the device in order to make it better through every iteration.
Tomi Engdahl says:
Benchmarks For The Edge
What works, what doesn’t and why.
https://semiengineering.com/benchmarks-for-the-edge/
February 7th, 2019 – By: Ed Sperling
popularity
Geoff Tate, CEO of Flex Logix, talks about benchmarking in edge devices, particularly for convolutional neural networks.
Tomi Engdahl says:
Benchmarks For The Edge
What works, what doesn’t and why.
https://semiengineering.com/benchmarks-for-the-edge/
Geoff Tate, CEO of Flex Logix, talks about benchmarking in edge devices, particularly for convolutional neural networks.
Tomi Engdahl says:
https://studio.kauppalehti.fi/schneider-electric-rakennusaineena-aly/ovatko-teidankin-it-ratkaisunne-reunalla
Reunalaskenta kasvaa 35,4 % vuosittain
Tietoturvaan, latenssin pienentämiseen ja lisääntyvän datan käsittelyn haasteisiin lääkkeeksi on kehitetty reunalaskenta (Edge Computing). Se tarkoittaa hybridiratkaisua, jossa suuri osa datamassasta esikäsitellään paikallisesti ja pilveen viedään vain tarpeellinen tieto.
Tutkimusyritys MarketsandMarkets arvioi reunalaskennan kasvavan vuosittain 35,4 % vuodesta 2017 vuoteen 2022. Edge-ratkaisujen etuja datakeskuspalveluiden käyttäjille ovat palveluiden modulaarisuus ja skaalattavuus joustavasti ja kustannustehokkaasti kulloiseenkin tarpeeseen. Reunalaskentaympäristöissä palvelut ovat nopeasti saatavilla lähellä käyttäjää.
Tämä on johtanut Edge-ratkaisujen esiinmarssiin. Isojen keskitettyjen pilvipalvelinkeskusten lisäksi tarvitaan verkon reunalla (Edge) hajautetusti sijaitsevia paikallisia pieniä mikrodatakeskuksia.
Tomi Engdahl says:
6G will achieve terabits-per-second speeds
https://www.networkworld.com/article/3305359/lan-wan/6g-will-achieve-terabits-per-second-speeds.html
Initial, upcoming 5G is going to be a disappointment, a University of Oulu researcher says. 6G, with frequencies up to terahertz, will be needed for true microsecond latency and unlimited bandwidth.
Mobile Edge Computing and Multi-access Edge Computing on the way
Pouttu said we will also begin to observe more of a new form of computing called Mobile Edge or Multi-access Edge Computing (MEC) to handle 5G as it transitions to 6G. That’s a network architecture where heavy processing takes place near people on server-cum-base-stations, but most of the final work, such as AI and problem modelling, happens in the mobile device or IoT device somewhere in the vicinity.
Tomi Engdahl says:
Using AI Data For Security
https://semiengineering.com/using-ai-data-for-security/
Pushing data processing to the edge has opened up new security risks, and lots of new opportunities.
Artificial intelligence is migrating from the cloud to IoT edge devices. Now the question is how to apply that same technology to protect data and identify abnormal activity in those devices and the systems connected to them.
This is a complex problem because AI is being used on multiple fronts in this battle, as well as for multiple purposes. The technology has advanced to the point where energy-efficient neural networks can be built on silicon, and that has raised a number of questions and issues that will need to be resolved. Among them:
What are the best approaches to keep data private or secure?
What are the best approaches to identifying and reacting to aberrations in data flow or other activity without impeding other potentially safety-critical functions?
What are the most efficient ways of adding in AI-based security without impacting overall power or performance?
The general consensus through the first half of 2018 was that AI training, as well as most inferencing, would happen primarily on massively parallel server farms. Edge devices would be collectors of raw data, but the vast majority of processing would happen in the cloud, with clean data pushed back down as needed. That perspective changed as the electronics industry began realizing just how much data would have to be moved if the data was not scrubbed, and how expensive and time-consuming that would be. And underlying all of this is concern about privacy rights for some or all of that data.
Tomi Engdahl says:
ARMv8.1-M Adds Machine Learning to Microcontrollers
The latest microcontroller architecture definition from Arm—ARMv8.1-M—stirs machine-learning hardware acceleration into the mix.
https://www.electronicdesign.com/industrial-automation/armv81-m-adds-machine-learning-microcontrollers?NL=ED-005&Issue=ED-005_20190220_ED-005_566&sfvc4enews=42&cl=article_1_b&utm_rid=CPG05000002750211&utm_campaign=23424&utm_medium=email&elq2=f3363fb86e984996bbac396824acd23c
Arm’s ARMv8.1-M architecture specification redefines its microcontroller offerings (Fig. 1). It includes the company’s Helium technology, which addresses machine-learning (ML) applications. Arm estimates that by 2022, more than 20% of IoT endpoint devices will have ML support.
Tomi Engdahl says:
Edge Computing Wants Smarter IoT Devices
https://www.eetimes.com/document.asp?doc_id=1334377
The evolution of edge computing means that internet of things (IoT) devices need more smarts to make decisions rather just shipping data to be crunched to the cloud — this means that more memory is required without increasing its footprint.
Adesto Technologies’ latest non-volatile memory (NVM) family is aimed at both consumer and industrial IoT devices that need to be able to do more than just ship data off to the cloud. Dubbed FusionHD, it’s an extension of the company Fusion family but with more intelligence at low power and increased density, said Paul Hill, senior marketing director for the company’s serial flash products group.
Tomi Engdahl says:
Arm Adds Helium Extension for Intelligent Edge Capability
https://www.eetimes.com/document.asp?doc_id=1334384
Arm has introduced its Armv8.1-M architecture with new vector extension called Helium, to bring signal processing and machine learning (ML) capability for local decision making in edge devices based on its Cortex-M series processors.
Helium is a new M-profile vector extension (MVE) designed from the ground up to give Neon-like performance to its Armv8.1-M architecture, expecting to deliver up to 15x more ML performance and up to 5x uplift to signal processing for future Arm Cortex-M processors. This will enable Cortex-M processors to be utilized where performance challenges have limited the use of low-cost and highly energy-efficient devices.
“Helium opens up signal processing and machine learning in a single core, enabling more edge compute capability in end devices,” said Rhonda Dirvin, a senior director for Arm’s automotive and IoT business said.
Tomi Engdahl says:
Edge Intelligence Grabs the Spotlight at Embedded World
https://www.eetimes.com/document.asp?doc_id=1334405
Nothing is beyond the limits of our imagination anymore, and what we are used to seeing in spy movies needs a massive upgrade, in order to go beyond what is now considered the norm. This was evident at Embedded World 2019, where the focus was edge intelligence and internet of things (IoT) security.
There were cameras everywhere capturing images, with demos showing each vendor’s capability in neural network processing, inference, and doing something with the information — including smart retail and surveillance. It seemed that everyone was saying: “See how much intelligence we can pack in the camera or sensor or edge-of-network system and see what we can do with it.”
Tomi Engdahl says:
Overview of edge computing and MEC
https://www.redhat.com/en/blog/overview-edge-computing-and-mec?sc_cid=7016000000127ECAAY
Tomi Engdahl says:
Edge AI
https://bootcamp.electronicdesign.com/edge-ai/?utm_rid=CPG05000002750211&utm_campaign=23930&utm_medium=email&elq2=1fc515cdd52247ecb0f093f4db884c44
As a global technology company with an ecosystem of experts at every stage of the product lifecycle, Avnet is uniquely positioned to help organizations of all types tackle the challenge of AI and edge computing. With our product development expertise and strong technology partners, you can have one partner help you turn AI into a real game-changer for your business—with the support it needs to compute at the edge and the security you need for a truly end-to-end solution.
Tomi Engdahl says:
Why Edge? Why Now? How the Rise of Edge Computing will Reshape the Data Center Landscape
https://www.cablinginstall.com/whitepapers/2019/03/why-edge-why-now-how-the-rise-of-edge-computing-will-reshape-the-data-center-landscape.html?cmpid=&utm_source=enl&utm_medium=cim&utm_campaign=cim_data_center_newsletter&utm_content=2019-02-11&eid=289644432&bid=2388741
Tomi Engdahl says:
10 best practices for edge computing
https://www.controleng.com/articles/10-best-practices-for-edge-computing/
When choosing, installing, and using an edge computing device for a manufacturing or process facility application, these 10 best practices can help.
Edge computing devices, so called for use at the edge of a discrete manufacturing or process control application, need to be properly selected, installed, and used, according to John Fryer, Stratus’ senior director of industry solutions, at the 2019 ARC Industry Forum in February.
Other names for edge computing devices may include industrial computers (IPCs), programmable logic controllers (PLCs), programmable automation controllers (PACs), or just industrial controllers.
Fryer provided 10 best practices for edge computing.
Agreement: Before starting a digital transformation project, get buy-in from operational technology and information technology staff. Doing so increases likelihood of success. Consider starting with a scalable project in an area of great distress, rather than with a seven-figure project.
Ownership: Don’t let information technology (IT) staff take over digital transformation or plant-related edge computing implementations. IT staff may not understand how patches and software upgrades affect high-availability, real-time needs on the factory floor.
Reliability and resiliency: Think through the reliability and resiliency requirements for edge computing and digital transformation. This includes maintenance and cybersecurity considerations.
Training: Ensure plant-floor personnel have training required to understand and implement the system.
Retention: Limit turnover of valued operations technology (OT) staff by valuing tribal knowledge. Complex, IT-based technologies may include a higher degree of risk because of training requirements.
Support: Ensure appropriate support for installed technologies. IT staff may not be willing to provided what’s needed at 2 a.m.
Hardware support: Select hardware that minimizes the need for long-term support.
Software simplicity: Avoid duplicative software with high license and support costs.
Lifecycle considerations: Take a total cost of ownership (TCO) view to lower associated risk and costs.
Application needs: Understand implementation needs; especially in remote or rugged locations of the facility or plant.
Tomi Engdahl says:
Gaining the edge in automation
https://www.controleng.com/articles/gaining-the-edge-in-automation/
Edge computing advances give users more options for architecting automation systems as well as flexible communication and programming choices.
Traditional automation architectures are built around centralized programmable controllers connected to remote field devices and instruments. However, this concept is shifting as computing power is progressively embedded near the edge of automation systems using new types of intelligent components.
Programming options
Programming for these edge components can take many forms. Traditional programmable logic controller (PLC) users usually will look for ladder logic or other IEC 61131-3 programming languages. However, a flowchart-based programming language is often better suited for the application.
Python or C/C++ might be preferred for more advanced calculations and data processing. Some edge computers can accommodate these programming languages and others
Universal translator
Communications flexibility is another hallmark of edge computing. An edge programmable industrial controller is equipped with various communication ports and supports a wide range of protocols so it can connect with numerous local intelligent systems such as PLCs.
Edge computing components support operations technology (OT) protocols such EtherNet/IP, Modbus, BACnet, those from OPC Foundation, and others. It also supports information technology (IT) protocols and development tools such as TCP/IP, simple network management protocol (SNMP), message queuing telemetry transport (MQTT), and Node-RED. This suite of interfaces effectively “flattens” and simplifies the system architecture
Familiar features
Classic automation systems relied on centralized and dedicated HMI hardware and/or software, sometimes proprietary and usually very expensive. Today’s users have been trained by their home computers, consumer devices and smartphones to expect rich HMI options almost everywhere.
Picking the right control system
When specifying edge computers ask if it can it:
Serve as a standalone controller and HMI for one machine?
Replicate one machine to many and enable them to communicate with each other and with local plant systems?
Extend control to a larger supervisory system and publish data to the cloud? If so, can it publish some data now and the rest later?
Tomi Engdahl says:
10 best practices for edge computing
https://www.controleng.com/articles/10-best-practices-for-edge-computing/
When choosing, installing, and using an edge computing device for a manufacturing or process facility application, these 10 best practices can help.
Edge computing devices, so called for use at the edge of a discrete manufacturing or process control application, need to be properly selected, installed, and used, according to John Fryer, Stratus’ senior director of industry solutions, at the 2019 ARC Industry Forum in February.
Edge computing best practices
Fryer provided 10 best practices for edge computing.
Agreement: Before starting a digital transformation project, get buy-in from operational technology and information technology staff. Doing so increases likelihood of success. Consider starting with a scalable project in an area of great distress, rather than with a seven-figure project.
Ownership: Don’t let information technology (IT) staff take over digital transformation or plant-related edge computing implementations. IT staff may not understand how patches and software upgrades affect high-availability, real-time needs on the factory floor.
Reliability and resiliency: Think through the reliability and resiliency requirements for edge computing and digital transformation. This includes maintenance and cybersecurity considerations.
Training: Ensure plant-floor personnel have training required to understand and implement the system.
Retention: Limit turnover of valued operations technology (OT) staff by valuing tribal knowledge. Complex, IT-based technologies may include a higher degree of risk because of training requirements.
Support: Ensure appropriate support for installed technologies. IT staff may not be willing to provided what’s needed at 2 a.m.
Hardware support: Select hardware that minimizes the need for long-term support.
Software simplicity: Avoid duplicative software with high license and support costs.
Lifecycle considerations: Take a total cost of ownership (TCO) view to lower associated risk and costs.
Application needs: Understand implementation needs; especially in remote or rugged locations of the facility or plant.
Tomi Engdahl says:
https://www.dna.fi/yrityksille/blogi/-/blogs/edge-computing-haastaa-mutta-ei-riko-
Tomi Engdahl says:
Spreading Intelligence From The Cloud To The Edge
https://semiengineering.com/spreading-intelligence-from-the-cloud-to-the-edge/
Explosion of data is forcing significant changes in where processing is done.
The challenge of partitioning processing between the edge and the cloud is beginning to come into focus as chipmakers and systems companies wrestle with a massive and rapidly growing volume of data.
There are widely different assessments of how much data this ultimately will include, but everyone agrees it is a very large number. Petabytes are simply rounding errors in this equation, and that soon will be replaced by exabytes at current growth rates.
“An autonomous vehicle will produce 15 terabytes of data per hour from sensors,” said Sumit Gupta, vice president of HPC, ML and AI at IBM, in a presentation at the recent Autonomous Vehicle Hardware Summit in San Jose. “When you take all the data from ADAS testing, that can get to 500 petabytes of data. Now, when we have to move it to a research center, the fastest way to move it is on a truck down the highway.”
Even storing that much data goes well beyond the capabilities of the largest supercomputer, which currently has about 200 petabytes of storage, Gupta said.
To handle all of these bits, at least some processing has to be done at the edge. It takes far too much time, energy and money to move it all—and the bulk of it is useless. But so far there is no agreement on how or where this will be done, or by whom. Cloud providers still believe hyperscale data centers are the most efficient tool to grind down the mountains of operational data produced by IoT devices every day. Device makers, in contrast, believe they can pre-process much of that data at or close to the source if they can put a smart enough, purpose-built machine learning inference accelerator in the device.
“The edge is becoming more and more intelligent,” said Lip-Bu Tan, president and CEO of Cadence. “Sending everything to the cloud is too slow, so you’re going to see the edge starting to take off. The hyperscale cloud will continue to explode, but for automotive and industrial the activity will be at the edge. The next big thing is the edge.”
Tomi Engdahl says:
Addressing the Challenges of Moving Security to the Edge
https://www.securityweek.com/addressing-challenges-moving-security-edge
For many organizations, the network perimeter has been replaced with a variety of new network edges. Many of these include unique challenges that can severely complicate an organization’s ability to maintain a consistent and manageable security infrastructure. These security challenges are two-fold.
The first involves implementing effective and consistent policy enforcement at an edge in spite of its unique network or platform configurations or functionality. The second is about creating consistent security between the various edges, not just for visibility, but to also ensure that policy changes and threat responses can be effectively coordinated across all edge environments.
While maintaining consistent visibility and control is table stakes for any security strategy, they are becoming increasingly difficult to maintain.
Securing the Expanding Edges of the Network
The network edge environments organizations need to secure and manage, some of their unique security challenges, and considerations for addressing those challenges include:
Cloud and multi-cloud — Each cloud platform has unique controls and management interfaces.
Enduser and IoT — The proliferation of IoT and enduser endpoint devices is another edge challenge for many organizations. These devices are not only getting smarter and faster, they are also highly mobile—and it’s not unusual for a single user to have multiple devices connected to the network simultaneously.
WAN edge — The new SD-Branch requires direct connectivity with other remote locations and datacenters, which means they require meshed VPN connections that not only allow them to connect, but that can also support performance-heavy and latency-sensitive business applications like VoIP and videoconferencing. And because they also include their own LAN—comprised of fixed and mobile devices, IoT devices, IaaS and SaaS connections, and multiple public internet links—they also require a full suite of security tools.
5G — 5G will introduce unprecedented speeds and interconnectivity that promise to further disrupt how we share critical information, deliver receive rich media, run data-heavy applications, and make real-time decisions. Interconnectivity between devices also has the potential to create a new and open edge cloud.
Tomi Engdahl says:
Spreading Intelligence From The Cloud To The Edge
https://semiengineering.com/spreading-intelligence-from-the-cloud-to-the-edge/
Explosion of data is forcing significant changes in where processing is done.
The challenge of partitioning processing between the edge and the cloud is beginning to come into focus as chipmakers and systems companies wrestle with a massive and rapidly growing volume of data.
“An autonomous vehicle will produce 15 terabytes of data per hour from sensors,” said Sumit Gupta, vice president of HPC, ML and AI at IBM, in a presentation at the recent Autonomous Vehicle Hardware Summit in San Jose. “When you take all the data from ADAS testing, that can get to 500 petabytes of data. Now, when we have to move it to a research center, the fastest way to move it is on a truck down the highway.”
Tomi Engdahl says:
Racing To The Edge
https://semiengineering.com/racing-to-the-edge/
The opportunity is daunting, but so are the challenges for making all the pieces work together.
The edge concept originated with the Internet of Things, where the initial idea was that tens of billions of dumb sensors would communicate through gateways to the cloud. That idea persisted until last year, when there was widespread recognition that even the fastest communication infrastructure available was still too slow and inefficient to stream video and other types of data to some remote location for processing, sorting, and storage—and then deliver some amount of information back to the device.
“If you go back a few years, everybody knows everything is going to cloud, everything’s going public cloud,” Paul Nash, group product manager for the Google Compute Engine in the Google Cloud. “But is it going to the edge? Where is it going? People are beyond assuming that any single definition of what the right way is will be accurate, so it depends a lot on where customers are, what their workloads are. And it really is this kind of multi-cloud thing where workload-by-workload and business-case-by-business-case, customers are trying to make the right decision about where things go.”
Cloud providers and large system vendors are calling this a hybrid cloud approach. Others are differentiating between the edge and the cloud. And within each of these, multiple different segments make it difficult to predict where this market is going. What is becoming clear, though, is that sending all data to hyperscale clouds such as Google Cloud or Amazon Web Services is grossly inefficient for some applications.
“Not everything will go to the cloud,”
Safety lives at the edge
“The edge includes a lot of the stuff where people are most concerned about things that can kill you, like cars and robots and medical devices,” said Kurt Shuler, vice president of Arteris IP. “These things can kill you two ways. One is a cosmic ray and the traditional functional safety use case, where it flips a bit and then it goes awry. The other way is everything works as intended, however what it does and what it decides to do from its neural net application is the wrong thing. There’s not a cosmic ray. There’s not a hardware safety problem. The safety of the intended function is bad. (There is a new specification out for that, ISO/PAS 21448:2019 Road Vehicles — Safety of the Intended Functionality.)”
This is where the edge gets complicated. Assisted or autonomous vehicles need a certain amount of internal processing and external communication, whether that is to another car or an edge server or cloud.
“There are a lot of differences in assumptions in the algorithms used to drive a car,”
All of this happens at the edge, and assisted driving is one of the key edge applications. But how all of this gets split up has a big impact on not only processing and reaction time, but also security. Until the edge is better defined, understanding where and how to implement security is difficult.
Reliability and interoperability at the edge
Another piece of the puzzle involves reliability of devices and systems operating at the edge. This is obvious for automotive, medical, and industrial applications. What is less obvious is how well supporting technologies such as 5G communications will impact their operation. An accident can occur because information housed in an edge cloud was either too far away to provide a response in time, or it couldn’t process the information fast enough.
The challenge is that multiple pieces of the edge need to work reliably together, and not all of them are progressing at the same speed or with the same goal in mind. As a result, when various quality control steps are combined, such as test, coverage becomes more difficult.