Flexible Embedded AI System with High-speed Neuromorphic Controller

Yoon, Young Il; Jang, Suyeon; Choi, Do Young; Lee, Seung Eun

doi:10.1109/isocc47750.2019.9078508

Cited by 4 publications

(2 citation statements)

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“…Furthermore, we will apply the multiple intellino for a multicore AI system. Since the multicore AI system needs a specific module for controlling the cores [41], we will take the efficient multicore management into account. We expect that the multi-intellino will enhance the performance of embedded AI systems and the flexibility against diverse applications.…”

Section: Discussionmentioning

confidence: 99%

Intellino: Processor for Embedded Artificial Intelligence

et al. 2020

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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.

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Section: Discussionmentioning

confidence: 99%

Intellino: Processor for Embedded Artificial Intelligence

et al. 2020

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“…Although federated learning between heterogeneous devices is the ultimate goal, parallel processing for federated learning among processors within a single device is prioritized, and the flexibility of the system can be provided by adopting this architecture. For the purpose of realizing the system with this architecture, the embedded AI cores need to be utilized and arbitrated by applying an additional controller [ 20 ]. In this paper, we propose an embedded AI system for federated learning that can be composed flexibly with AI cores depending on the application.…”

Section: Introductionmentioning

confidence: 99%

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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