EdgeAI: Next Wave of AI Computing

In our technology-driven world, data has emerged as the currency of utmost importance. We have witnessed some remarkable advancements in fields of Artificial Intelligence recently. Edge Computing, though not a very new concept, has now become a major game changer in the field of AI. Various industries, including Surveillance and Healthcare, now rely on EdgeAI for their mission-critical operations because of its benefits, discussed further.

Before delving into EdgeAI, let us first grasp the general functioning of the pipeline in a conventional AI application.

Billions of devices are connected worldwide. These endpoints (or device) could be your mobile phone, cameras used in drones or amazon’s Alexa. To make these devices robust and intelligent, data collected from these endpoints is under continuous transmission via different networks, and most of them relying on various cloud services such as AWS, Google cloud and Azure for processing data and generating meaningful insights and responses. These cloud based pipelines perform various operations on the data, from processing of data, cleaning, storage, running ML inferences, training and many others.

Cloud based AI pipeline

General Architecture of CloudAI

1. Endpoints are connected to the internet.

2. Data generated on these devices is sent to the cloud.

3. Based on the requirement of the application cloud performs several operations ranging from processing of data, cleaning, storage to running AI or analytical operations on the data.

4. Response generated in the cloud is sent back to the device as an output to the end-user.

Although, such a pipeline looks very streamlined and pretty straight forward, it harbors challenges concerning latency, response time, and connectivity due to its reliance on request-response architecture. In mission critical environments where inference-time and immediate response plays a vital role, cloud infrastructure is prone to multiple failures.

The Solution: AI at the EDGE.

What is EdgeAI ?

EdgeAI is a combination of Edge computing and Artificial Intelligence, in which the endpoint is placed at a close proximity or (edge) where the data is being generated. In this approach, the AI application as a whole (including inference engines) is deployed at the edge, which can run the inferences locally, thereby generating real-time responses with almost zero latency. Another problem that Edge Computing solves when compared to cloudAI is the cost of transmission of data. In a cloud architecture, continuous transfer of data adds to the cost of operations, thereby making it more expensive.

While in an edge architecture, there is minimalistic use of cloud service, mainly in 2 cases, thereby reducing the cost of operation as a whole:

1. When it encounters an unknown or low-confidence data. This data is sent back to the data centers where it is then analyzed and further processed to retrain the model.
2. Deployment of a new or retrained model.

EdgeAI Architecture

Hardware

Neural nets are resource hungry and require higher computational power to run. With advancement in embedded technology, we now have specialized hardware for edge computing that allows to attain high and accelerated performance using GPUs and TPUs in a compact form factor. This exponentially reduces the size and storage cost and also improves inference throughput and enables parallel processing. Some of the hardware popularly used in industries include Nvidia Jetson series modules, Google Coral and Raspberry Pi.

Edge Computing Hardware

Benefits of EdgeAI:

1. Latency: In mission critical environments, time of response is very crucial for a successful operation. In an edge based computing environment since all the operations take place locally, the delay gets reduced. This helps to achieve real time performance whereas in a cloud setup since the data has to move back and forth, it produces high latency and hence may cause the operation to fail.
2. Security: With such a massive amount of data being generated each day, privacy of data has become a vital issue. By performing operations locally on devices and minimized data transfers to external networks, EdgeAI ensures the preservation of privacy.
3. Reliability: A major concern with cloudAI is the availability of internet as data needs to be transferred to the cloud for processing. Whereas, edge based setup does not require internet for running inference.

Popular use case

1. Surveillance:

Safety of public and property is always a concern. With advancement in AI, security cameras are now able to run AI inferences and have become capable of detecting threats like weapons, trespassing and many others. With these inferences running on the edge, it becomes possible to raise alerts immediately and minimize damage.

2. Autonomous Vehicles:

Decision making is vital when it comes to self-driving cars or ADAS, as they must perceive the environment, maintain the right speed, and brake effectively. These critical tasks demand minimal latency and uninterrupted processing to prevent fatal accidents, making EdgeAI indispensable in such scenarios.

3. Healthcare:

AI powered imaging systems are now being installed in many hospitals that help in early detection of disease and tumors with accelerated processing power using GPUs. Most of these data are high resolution images hence sending them to cloud for processing will be very expensive and time consuming. Leveraging edge computing reduces cost and time of operation while ensuring data privacy, making it an optimal solution for healthcare applications.

4. Smart Cities

The rapid advancements in computer vision and NLP have paved the way for smart cities, automating tasks such as parking management, weapon detection, and crowd control. Real-time response is crucial for seamless operations, making edgeAI an advantageous choice in enabling swift and efficient processing at the local level

Conclusion

EdgeAI as a mechanism is an efficient choice for Event-Driven applications, offering a multitude of benefits over traditional cloud-based AI pipelines. These hardware are now capable of performing parallel processing, optimization, and the execution of large and complex Neural Nets, yielding maximized throughput. Furthermore, their compatibility with various sensors enables the collection of diverse forms of data, fostering flexibility and adaptability across different sectors, unlocking new possibilities for a data-driven future.

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