Smart imagers modeling and optimization framework for embedded AI applications

Cubero, Luis; Peizerat, Arnaud; Morche, Dominique; Sicard, Gilles

doi:10.1109/prime.2019.8787750

Cited by 1 publication

(2 citation statements)

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“…Both frameworks provide quantization of floating-point arithmetic to integer arithmetic and applying memory constants for scaling the neural network for each device. The framework of [ 28 ] has two fundamental features. One is a neural architecture search (NAS).…”

Section: Related Workmentioning

confidence: 99%

“…When applying either AI or other features in embedded systems, searching and selecting hardware specifications that meet the performance requirements of each application are as important as designing the embedded system. Hence, many studies have been performed to build a framework to simulate and design the system efficiently [ 21 , 28 , 29 , 30 ]. The research of [ 29 ] is a suitable example of this research category.…”

Section: Related Workmentioning

confidence: 99%

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A Multi-Core Controller for an Embedded AI System Supporting Parallel Recognition

Jang

Yoon

et al. 2021

Micromachines

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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.

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Section: Related Workmentioning

confidence: 99%