[Tech]Understanding On Device AI and Its Application Landscape

#AI #OnDeviceAI #Jetson #TELROP #Cloud #NVIDIA #Fast

The field of AI is witnessing daily advancements, with news and surprises about new technologies continually emerging. Particularly, areas such as Large Language Models (LLM) and vision recognition stand out, likely due to heightened interest. However, applying these advancements in real-world settings poses a separate challenge. "On Device AI" refers to AI applied to edge devices, which are hardware that interacts directly with the user environment.

On Device AI vs Cloud AI

While AI utilizing cloud computing is referred to as Cloud AI, systems that can derive appropriate conclusions without the need for cloud connection or assistance are termed On Device AI. Combining On Device AI with Cloud AI could potentially offer complementary services.

Key features of On Device AI include:

Rapid Response
Without the need for a connection to cloud servers, AI functionalities and inference results can be obtained immediately, avoiding communication delays.

Enhanced Data Security
Processing data within the device, rather than transmitting it to cloud servers, strengthens data security and mitigates risks of personal information leakage.

Reduced Energy Consumption
Energy required to transmit data to the cloud is also minimized.

Implementing On Device AI with Jetson

On-device learning-inferencing offers advantages such as privacy protection, reliability, and real-time processing. However, due to the limited hardware performance of edge devices, pipeline optimization through appropriate use of hardware blocks is essential. Numerous NVIDIA Jetson vision processing projects have achieved optimization by utilizing each hardware block effectively.

Using On Device AI in conjunction with Cloud AI

Despite the distinct features of On Device AI, to implement high-level AI actively being developed up to now, using Cloud AI allows leveraging the strengths of both. That is, On Device AI resources can be used for immediate, real-time needs, while Cloud AI can be utilized for intricate processes requiring higher power or for enhancing AI performance through learning.

Edge AI technology, responsible for On Device AI, is expected to be widely adopted across various industries due to its real-time processing capabilities, energy efficiency, and continuous demand. It is becoming increasingly effective in diverse applications, including autonomous vehicles, automotive data collection devices, factory automation, smart cities, and more recently, in robots and drones.


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