Embedded Landing and What is it ?

Introduction

“Landing” is the word that is used by members of the AI community to describe the deployment of an AI algorithm to production in the last step of the data science process. But there is yet another term associated with this word, and it is known as Embedded Landing.

Embedded Landing involves embedding light-weight AI algorithms within the firmware, the application-specific integrated circuits (ASIC), and bare metal systems without any form of cloud backup. The distinction between this and edge AI lies mainly in the factor of permanence.

Why Embedded Landing Remains in the Shadows:

Embedded Landing is an unpopular field for various reasons. For one thing, it does not have that excitement factor like generative AI or large language models do. People hardly ever make a viral sensation out of demonstrating how a microcontroller is able to write poetry. Secondly, Embedded Landing is always invisible to the outside world. Nobody notices that there is an algorithm in their hearing aids, tire pressure sensors, or other household devices. Thirdly, it is very challenging work.

The Unique Technical Challenges:

To make a model “land” on hardware is to be under strict limits. Engineers have kilobytes of RAM to work with but not gigabytes. The power consumed by the inference should not exceed microwatts. It also has to be deterministic and verifiable. The engineers have to rule out randomness.

As a result, engineers often rely on Quantized Boolean networks, assembly-level decision trees, or very small convolutional kernels. They must prove the performance of these systems across all possible inputs. Unlike cloud AI, where 99% accuracy is acceptable, embedded systems may require complete reliability.

Critical Applications That Depend on Embedded Landing:

Embedded landing plays an important role in supporting essential infrastructure. For instance, pacemakers have artificial intelligence (AI) systems that detect irregular heartbeats. Wireless updates to such medical devices is not always possible due to security reasons. Seismic shutoff valves used in pipelines are controlled by embedded AI systems. They need to be able to make split-second decisions based on old and unreliable 8-bit microprocessors.

Airbag classification systems in cars offer yet another good example. Airbags use AI to distinguish crashes from other types of impact.

Conclusion

Embedded Landing may go largely unnoticed by artificial intelligence (AI) researchers, yet it is very crucial. It provides some of the best ways to deploy an AI system. With the rise in the use of Internet of Things (IoT), there will be a requirement for more engineers to implement embedded AI.