Cutting The Cord: How Edge Intelligence Is Enabling The IoT To Go Where Cloud Can’t

Adding more cloud compute won’t solve the problem of managing exponentially growing quantities of data.


In a world where data’s time to value or irrelevancy may be measured in milliseconds, the latency introduced in transferring data to the cloud threatens to undermine many of the Internet of Things’ most compelling use cases.

Think of data as the fuel that powers our new decision-making engines – fail to get the fuel to the engines fast enough and the engine splutters and dies. Meanwhile, that fuel is constantly decreasing in quality and usefulness. But solving this problem isn’t a case of making bigger pipes to carry the world’s data or increasing the cloud’s compute capability. The issue is one of balance, or as we see now, imbalance. In 2016 the IoT generated 1.6 trillion gigabytes of data, and Cisco estimates this will rise to 500 trillion gigabytes by the end of 2019, growing exponentially in years following. As the data grows, the friction it encounters along its journey will increase.

Physical problem, physical solution
It’s clear that we need a new path; one that isn’t simply about adding more central cloud compute capability to the data center. While we do need more compute, it needs to be strategically placed so we co-solve the digital and physical distance challenge that increasing data congestion is causing. As distance increases network latency, the obvious answer is to decrease the real-world distance between where sensor data is collected and where it is computed. This means enabling compute in the most physically appropriate location – whether that is on device, in network devices such as IoT gateways or in the traditional cloud.

While there are other factors to consider such as the level of compute required, privacy and security needs and how much latency can be tolerated, the general principle of enabling a more intelligent ‘edge’ is a now a compute priority. By adding more compute power where it counts, we will be able to scale the IoT and not exacerbate the latency issues – and the extensive costs of transferring raw data – that will disrupt value.

A recent study by McKinsey concluded that compared to the gains offered by cloud-based computing, where most gains were felt in the technology sector, edge compute provides a unique opportunity to enable a far broader spread of industries. The study identified 107 unique edge computing use cases across 11 industries – from improved retail processes to the tracking of humanitarian inventories in disaster situations. In particular, the study highlighted the hugely positive effect edge compute capability can have on environments such as mines and oil rigs, underwater applications and other adverse environments that may lack the safety net of always-on internet connectivity – or even always-on electricity.

Today, the majority of devices derive their value from the fact that they are connected into a huge network of intelligence. Smart speakers, for example, give the illusion of in-built intelligence yet actually rely on powerful AI algorithms in the cloud to translate anything other than an activation keyword. In this case, a delay while the device waits for an answer is unlikely to present a life or death situation. Yet consider a medical device or app being used in a remote environment: a data bottleneck and poor connectivity might be far more serious. It’s therefore fundamental that a capable edge device should have the power and resource to perform – and to continue to perform – its primary function independently.

True IoT edge intelligence is a complex challenge
Yet ‘adding intelligence’ isn’t as simple as putting a more powerful capable processor into every IoT device: increase the compute capability and the complexity, cost and power consumption can skyrocket. Autonomous vehicles, perhaps today’s most complex example of an edge device, must be capable of making sense of the world around them via powerful machine learning (ML) processing of massive amounts of data received from multiple sensors in real time. To put this requirement into context, the autopilot software in a Boeing 787 Dreamliner comprises around 14 million lines of code. A Level 5 (fully autonomous) self-driving car’s software is likely to approach 1 billion lines. That means packing supercomputer-level power in a device that doesn’t take up half the car’s trunk, emit more heat than a Death Valley sun and account for most of its cost.

Through programs like Project Trillium, Arm is committed to finding ways to add intelligent compute capability such as artificial intelligence (AI) into even the smallest IoT devices while retaining the trademark energy-efficient, cost-effective benefits our technology is known for, helping to bring the benefits of Arm-powered compute to the IoT’s most challenging environments.

Security is another key concern: as the frequency, severity and complexity of malicious attacks against transmitted data increases, security is becoming paramount. Processing information at the edge, and transmitting only the useful data, mitigates much of the risk yet does not absolve us of security concerns. Even without connectivity to any external system, security is a critical component for protecting integrity and confidentiality of the data and firmware stored on the device, as well as controlling lifecycle management such as over-the-air (OTA) firmware upgrades. The Arm Platform Security Architecture (PSA) is our contribution to ensuring that devices remain secure, providing a recipe, based on industry best-practice, that allows security to be consistently designed in at both a hardware and firmware level. Arm TrustZone security technology simplifies IoT security and offers the ideal platform on which to build a device that adheres to PSA principles.

Facing adversity
Finally, let’s not forget that in enabling powerful compute at the logical extremes of the network there will be adverse physical conditions to deal with. A device hardwired within a temperature-controlled warehouse might enjoy a long, easy life with device provisioning and updates performed locally, while a wave-powered gas sensor placed on the bed of the North Sea is likely to face a far greater challenge – and keep performing its duties, even if network or power connectivity is lost for long periods of time.

Already, we’re seeing devices capable of incredible edge compute in harsh conditions: take Oxford Nanopore’s MinIT, a portable Arm-based device for DNA and RNA sequencing that does away with uploading tens of gigabytes of locally-captured data to a cloud server by processing a sample locally. Notwithstanding any latency issues, the cost of communications alone in remote and harsh environments such as these would make the use of any device incapable of this kind of on-board processing commercially impossible.

The enablement of compute at the edge isn’t just one more step towards a world of a trillion devices; if we ever want to realize the ambitions of the IoT, it’s imperative. The IoT has evolved as its use cases have expanded, and so has the need for these devices to perform independently. IDC predicts that by 2020, IT spend on edge infrastructure will reach up to 18 percent of the total spend on IoT infrastructure, and for this to become a reality we need the entire spectrum of compute capable, fired up and ready, from the smallest device to the highest performance data center.

Learn more by joining my Tech Talk at Embedded World, “Think Local: How to Migrate Intelligence from the Cloud to Embedded Devices at the Edge.”



Stephen Su says:

Like this article.

Ayush Gupta says:

Excellent !
It provided a great insight

Pavan Bathla says:


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