I. Training Background and Objectives

In our ongoing business development and technical exchanges, we have observed that both within our team and in communications with clients, there is frequent overlap and confusion among terms such as “embedded AI,” “AI + embedded,” “edge AI,” “on-device AI,” and “cloud AI.” This terminological ambiguity not only increases communication overhead but may also lead to misunderstandings during the evaluation of technical solutions. This training session aims to establish a unified technical framework for LeaKin Technology’s engineers and business teams, clarifying the core dimensions and boundaries of each concept, thereby enhancing internal collaboration efficiency and the professionalism of external communications.

II. Clarification of Core Concepts: Five Terms Do Not Belong to the Same Dimension

When engaging in technical communication, the primary principle is to distinguish among “system architecture,” “industry convergence,” and “deployment location.”

1. Embedded AI The key lies in the “system architecture,” emphasizing that AI capabilities are deeply integrated into the device itself. The device inherently incorporates typical embedded‑system features such as an MCU, SoC, RTOS, and sensors, with AI serving as its core functionality. For example, an STM32 board paired with an IMU sensor can perform on‑device motion recognition—such as detecting a wave or a stationary pose—enabling the device to handle perception, decision‑making, and response all within its own hardware.
2. AI + Embedded Systems : At its core lies “industry convergence.” This is a broader concept than embedded AI, emphasizing the integration of AI technologies with the embedded‑systems industry, workflows, and ecosystems. It encompasses not only on-device deployment of lightweight models, but also AI‑assisted development—such as using large models to generate STM32 driver code or analyze Keil compilation errors—and enables devices to leverage cloud‑based large models via APIs for natural language interaction.
3. Edge AI The key lies in the “deployment location.” The emphasis is on running the model at edge nodes close to where the data is generated, rather than on a remote cloud platform. Edge nodes can include ARM‑based development boards, edge industrial PCs, or even GPU‑equipped edge servers. For example, after a factory‑floor camera captures an image, the local edge industrial PC performs defect detection.
4. On-device AI The core idea is to “be closer to the device itself.” It is a subset of edge AI, emphasizing that models run directly on the endpoint device rather than on a nearby edge node. For example, a smart lock performs face detection locally, or a pair of headphones handles voice activation on-device.
5. Cloud AI The core principle is “remote centralized computing power.” End devices serve solely as data collection and command‑receiving terminals, while the core model runs on servers or cloud platforms. Its advantages include the ability to handle large models and facilitate easy updates; its disadvantages are heavy reliance on network connectivity and relatively high latency.

III. Common Communication Pitfalls and a Guide to Clarification

When communicating with clients or across departments, please pay particular attention to the following common points of confusion:

1. “Letting a large model write code for me” is not the same as “embedded AI.” When customers or engineers mention using AI to generate peripheral initialization code or to refine microcontroller state-machine logic, this falls under the category of “AI + embedded systems” development assistance and does not imply that the model has been deployed to run directly on the device.
2. The Overlap and Differences Between Edge AI and On-Device AI : Edge-side AI is necessarily edge AI, but edge AI is not necessarily edge-side AI. If the model runs on the camera’s own SoC, both terms apply; if the model runs on a campus gateway that aggregates data from multiple cameras, it qualifies only as edge AI.
3. The Multiple Attributes of Complex Systems Real-world industrial projects often span multiple conceptual layers. For example, an industrial monitoring system might involve: a microcontroller performing initial anomaly screening (embedded AI/on-device AI); an on-site gateway conducting multi-device trend analysis (edge AI); and a cloud-based platform handling model training (cloud AI). The entire R&D process is supported by AI (AI + embedded systems). When communicating, it’s essential to clarify which layer of the system is being discussed.

IV. Practical Recommendations for LeaKin Technology’s External Communications

1. Requirements Clarification Mechanism When aligning with customer needs, start by posing the core question: “Are we currently discussing ‘what the system is,’ or ‘where the model is running’?” This helps quickly clarify the customer’s technical requirements and prevents getting bogged down in conceptual tangents.
2. Presentation Guidelines for the Proposal When drafting technical proposals or PPTs, the five terms listed above must not be used interchangeably as synonyms. Instead, you must accurately specify, based on the actual architecture: whether the focus is on the device’s intrinsic intelligence (embedded AI), on a low‑latency architecture that keeps data local (edge AI/on‑device AI), or on enhancing R&D efficiency (AI + embedded).
3. Manage customer expectations When a customer proposes “implementing edge AI,” engineers must first confirm the customer’s compute‑power budget and hardware platform, clarifying whether a gateway‑level edge server or an MCU‑based on‑device TinyML solution is required. This enables the delivery of a precise quotation and implementation roadmap aligned with LeaKin Technology’s technology stack.

V. Conclusion

Precise articulation of technical concepts is a direct reflection of an engineer’s professional competence. We hope that all colleagues at LeaKin Technology will, in subsequent code development, solution reviews, and client communications, rigorously adhere to the aforementioned conceptual framework, leveraging clear technical language to build our core competitive edge and earn our clients’ trust through professionalism.