Intelligent Edge: A New Era of Network Evolution
Organizations started embracing technology back in the 70s and haven’t looked back since. Fast forward 50 years and advanced technologies like Artificial intelligence (AI) and the Internet of Things (IoT) are being introduced and widely adopted, which is transforming how businesses interact with customers.
Here’s how we got there:
Initially, computing resources were centralized and based on large-scale mainframe (a.k.a. big iron) computers. Next, we saw the introduction of personal computers and standalone servers (a.k.a. client-server), which shifted resources nearer to users.
Then the Internet came along and businesses discovered the usefulness and effectiveness of cloud computing, prompting many companies to shift most of their resources there.
The next swing of the pendulum is on the verge, but it’s unlikely to consist of another migration of IT resources away from centralized facilities. Instead, it will bring about a new era of network evolution where we witness tiny machine learning (TinyML) and edge computing coming together to underpin the future of smart and autonomous devices at the intelligent edge.
Around 10-15 years ago, the edge was pretty clear… it was a router at the edge of a terrestrial network (a.k.a. The internet) or a switch or access point at the edge of an enterprise network (always on devices). But now, with 5G, IoT and some devices being off network most of the time, the edge demarcation point is moving off the network to the device.
Understanding the Intelligent Edge
The intelligent edge redefines how we transform data into actionable insights in real time (or near to it), closer to where data is being generated and captured.
One of its main appeals is the ability to reduce response times, bandwidth needs, and security risks by leveraging edge computing to support edge devices on analytics performed by artificial intelligence and machine learning.
Since all computer storage and processing are performed at the edge of the network, data doesn’t need to be pushed up to the cloud or a data center to be processed. Thus, devices at the edge are equipped with computing power and intelligence to manage various kinds of data, which previously would have been sent to a server in the cloud. This enables businesses to become more decentralized and self-contained – no longer vulnerable to interruptions due to a faulty network or limited connectivity which could hamper data processing procedures.
Although everyone is seemingly talking about it for the first time now, the underlying technology of the intelligent edge has been emerging for decades.
The origins of edge computing can be traced back to 1998, when Akamai launched the first static Content Delivery Network (CDN). They foresaw how congestion would cripple the Internet and developed a network to deliver cached images and videos using distributed servers that were located closer to the end user. This was breakthrough thinking at the time. Now the Internet could not function without CDN.
In their 2002 paper, Globally Distributed Content Delivery , Akamai explained, “By caching content at the internet’s edge, we reduce demand on the site’s infrastructure and provide faster service for users whose content comes from nearby servers.” and how right they were! Content Delivery Networks are a big deal for the Internet, and even more so for mobile users. By 2028 the CDN market is forecast to reach $38B worldwide.
The late 1990s and early 2000s saw the rise of peer-to-peer (P2P) systems. Note these networks had already been in use in many application domains, but the architecture only became popular when it was adopted by the file-sharing system Napster.
The overlay of P2P introduced the notion of proximity routing to prevent slow downloads over long-distance servers. Namely, in P2P, a network of peers could locate other members and partition tasks or workloads between them to download content much faster.
Around this time, we also witnessed the launch of Amazon Web Services (AWS), the first public cloud service renting computing and storage resources to end-users.
In 2012, Cisco coined the term “Fog Computing,” which describes a form of distributed computing that brings computation and data storage closer to the network edge, where several IoT devices are located [Source: Wikipedia].
According to 360 Market Updates , the global Fog Computing market size is exploding right now. It is expanding at a compound annual growth rate (CAGR) of 58.94%, and may exceed a market size of $1B by 2031. This is to be expected given its relation to edge computing.
This brings us to the present day, the era of edge computing and machine learning. Things are gradually shifting toward an Intelligent Edge. A vast amount of data is already being generated by IoT devices, exclusive of data centers, which makes intelligence close to end-users.
The Convergence of Edge Computing and Machine Learning
Breakthroughs in deep learning have catalyzed the boom in AI applications and services. Rapid advances in mobile computing and the Artificial Intelligence of Things (AIoT) have resulted in a drastic rise in mobile and IoT devices connected to the internet that generate massive amounts of data at the network edge.
The success of AI and IoT technologies has created the demand for AI frontiers to be pushed to the network edge to realize big data’s full potential. A promising concept to support compute-intensive AI applications on edge devices is edge computing.
The convergence of machine learning and edge computing presents an opportunity to provide a comprehensive solution for intelligent devices, where machine learning brings the brain and edge computing offers the brawn, handling the localized data processing.
This convergence allows for more sophisticated and real-time decision-making capabilities since AI-powered programs can be closer to data points like sensors, cameras, mobile phones, etc., enabling them to collect insights swiftly, recognize patterns, and take action without requiring a round-trip to the cloud.
The introduction of TinyML frameworks also plays a significant role despite being in its early stages. The ability to implement machine learning techniques in low-energy systems makes it possible to bring machine learning to the edge in an extreme way; thus enabling the development of novel applications and solutions previously considered impossible due to resource constraints or performance issues.
The Role of Silicon Advances in Shaping the Intelligent Edge
In 1965, Gordon Moore, co-founder of Intel, shared an observation he made of the long-term trend in how technology is transforming. This observation became known as Moore’s law.
Moore’s law posits the number of transistors on a microchip, doubles about every two years, though the cost of computers is halved. This has held true for the most part in the computing world too. Since Intel introduced the microprocessor in 1971, computing capabilities have improved at breathtaking speeds. Computer chips are millions of times more powerful today than were fifty years ago.
However, despite the processing power increasing exponentially over the years, the fundamental architecture of the computer chip has remained unchanged until recently. For the most part, innovation in silicon has involved minimizing the size of transistors in order to fit more of them onto integrated circuits, but things are changing.
The revolutionary potential of AI has been reshaping the computing landscape as it’s been known. Due to its aptitude for real-time learning, AI offers a plethora of opportunities for intelligent devices to enhance user experience and push the boundaries of smart technology across various industries.
But fully utilizing these capabilities calls for immense computational resources. Traditional chips are not capable of meeting these needs. The demands of AI have focused attention on the physical limits of producing ever-smaller transistors.
The pace of chip density growth has slowed, as has the power consumption per unit area. Consequently, larger chips run the risk of overheating when faced with the computing demands of AI. Thus, the future of silicon-based electronics depends on the adoption of innovative silicon architectures that prioritize low power consumption and high performance by employing both layered and parallel processing designs.
New silicon is in development, and we expect great things, but the semiconductor vendors have their work cut out for them. That’s because as soon as they give us resources, we will use them and want more. Improved performance and energy efficiency of new silicon architectures will enable the massive computation resources required for AI applications, creating limitless possibilities for intelligent edge devices to function independently or connected.
The Intelligent Edge: Today and Tomorrow
Nowadays, AI is predominantly deployed as a cloud service, which works extremely well for many commercial applications. However, the next frontier of technological innovation will be a new class of stand-alone, smart devices capable of localized decision-making.
Such devices don’t only collect or generate data; they are also active interpreters and decision-makers, able to take immediate action on the data they process in real time. These devices are typically designed for use cases with low-power settings where cloud connectivity is sparse, unavailable, or unviable.
Whether it’s consumer gadgets, specialized industrial equipment, or sensors on a vehicle, stand-alone smart devices embody a shift toward a decentralized future where intelligence is distributed and autonomous.
The IoT Revolution and the Intelligent Edge
IoT has become one of the most important technologies in recent years. Low-cost computing, the cloud, big data, analytics, and mobile technologies have all made it possible for physical objects to exchange and gather data with minimal human intervention. As a result, we can now connect the real world to the digital world through embedded devices.
However, conventional IoT solutions rely on cloud computing. Namely, the vast amount of data generated from IoT devices is uploaded to the cloud for analysis. Since IoT devices are pretty dumb, their digital functionality is generally limited to: detect something, turn it into data, wake up a radio, send data to cloud, go back to sleep, or something similar.
This raises concerns since the rise in IoT devices means communication costs, bandwidth, and latency become more expensive. Ultimately, cloud-based solutions are unsuitable for real-time and time-sensitive applications – you’ve also got to consider that IoT has structured and unstructured heterogeneous data that depend on advanced tools for its management.
Currently, we’re advancing to IoT 2.0, or if you will, smarter IoT. The integration of edge computing with AI/ML has the potential to provide a much faster route to converting data into actionable insights to deliver better business outcomes, optimized operations, and enhanced products and experiences since implemented across dispersed environments. All without the cost and delay of uploading masses of data and burdening the cloud with computation.
Challenges and Opportunities
Undoubtedly, organizations that embrace the intelligent edge will gain an integral advantage in driving growth and revenue, but it will not be without its obstacles…
For starters, implementing intelligent edge solutions is hard. There are three crucial elements that must be met for the development and operationalization of intelligent edge solutions:
- Endpoint Management: The more edge devices deployed, the more dispersed the IT environment becomes. To ensure the systems perform as expected, a secure endpoint management system is required to allow teams to monitor, patch, and update devices remotely. It also enables the team to identify any security or storage issues and manage them at scale.
- Automated Lifecycle (DevOps): Intelligent edge solutions often use cloud applications to process and deliver data to users, meaning an automated lifecycle is necessary to continuously manage, update, and deploy these applications at scale.
- ML Operations (MLOps): Problems with IoT networks (e.g., device failure, poor coverage, etc.) may cause the acquisition of irrelevant, redundant, and missing data. All of these features may reduce the model’s accuracy while increasing the execution time and the computational complexity of the model. Thus, machine learning models must be continuously trained, tested, deployed, and evaluated to maximize their long-term value.
At the time of writing, due to there being no off-the-shelf, general-purpose AI/ML framework capable of analyzing, understanding, and using any kind of data, selecting the appropriate model or models for the unique data processing needs of each use case, takes a lot of work.
This serves as a barrier to entry for many businesses keen on getting in on the action. Instead, they’re forced to spectate the rise of intelligent devices simply because they lack the expertise to assess and select the ideal machine learning framework and make the best design decisions for each stage of the pipeline.
The feasible solution would be to hire the experts required to complete an internal development, but for many businesses, this is a difficult feat to justify for an unproven business case; thus, further accentuating the fact a number of opportunities are being passed up.
While machine learning framework selection and other things, such as data mapping, add new complexity to the development of intelligent edge devices, the challenges do not end there…
There’s also the all-too-familiar challenges traditionally associated with embedded systems, such as limited processing power, memory constraints, privacy and security. Tackling all of these problems requires a wide-range of skills, which can prevent many businesses from partaking in this new era of network evolution.
But it doesn’t have to be this way…
Conclusion: The embedUR Advantage
While not insurmountable, the technical challenges of building intelligent edge solutions depend on a high level of specialized expertise to overcome. However, most startups and some established companies lack the depth of engineering resources necessary to cover all three aspects.
Adding additional engineers does not do much to solve this problem in the absence of effective design leadership. Thus, most organizations often have to collaborate with embedded systems companies with the engineering prowess to build their solutions and the systems architect to make prudent design choices.
As embedded experts with a knack for cramming highly optimized code into incredibly small spaces, this is a familiar landscape for us. With our edge computing and communications background, we’ve been tackling similar integration challenges that arise from intelligent edge solutions for the past ten years.
With nearly twenty years of experience in embedded wireless solutions, and ten in IoT, embedUR is equipped with the engineering know-how and resources to scale this next technological challenge.
Note the problem for engineering managers and CTOs with limited resources extends beyond simply understanding new technologies; it also involves effectively integrating them into product pipelines and meeting aggressive schedules.
Here’s where embedUR comes in. We are the perfect partner for navigating this challenging environment because of our extensive knowledge of embedded systems, Edge Computing, and cutting-edge technologies like TinyML.
Give us a call, lets explore how to bring your big idea to market.
