Abstract
Recent advances in artificial intelligence (AI) technology encourage the adoption of AI systems for various applications. In most deployments, AI-based computing systems adopt the architecture in which the central server processes most of the data. This characteristic makes the system use a high amount of network bandwidth and can cause security issues. In order to overcome these issues, a new AI model called federated learning was presented. Federated learning adopts an architecture in which the clients take care of data training and transmit only the trained result to the central server. As the data training from the client abstracts and reduces the original data, the system operates with reduced network resources and reinforced data security. A system with federated learning supports a variety of client systems. To build an AI system with resource-limited client systems, composing the client system with multiple embedded AI processors is valid. For realizing the system with this architecture, introducing a controller to arbitrate and utilize the AI processors becomes a stringent requirement. In this paper, we propose an embedded AI system for federated learning that can be composed flexibly with the AI core depending on the application. In order to realize the proposed system, we designed a controller for multiple AI cores and implemented it on a field-programmable gate array (FPGA). The operation of the designed controller was verified through image and speech applications, and the performance was verified through a simulator.
Highlights
Recent advances in computer and semiconductor process technology have developed artificial intelligence (AI) technology
This paper proposed a controller for parallel recognition among AI
The controller independently operated multiple AI cores and flexibly configured the number of AI cores according to the requirements of the application to be applied
Summary
Recent advances in computer and semiconductor process technology have developed artificial intelligence (AI) technology. The general machine learning systems adopt the architecture in which the central server processes and trains most of the data and delivers the result to the client [5]. Unlike the previous model, federated learning adopts an architecture in which the clients take care of data training and transmit only the trained result to the central server. This property makes it possible for the system to operate with reduced network resources [8,13] and reinforced data security [14,15,16] According to these characteristics, federated learning is being applied to a variety of applications such as smart factories [17], edge device applications [18,19], and end user privacy-sensitive applications.
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