RESUMO
Recently, neuromorphic computing has been proposed to overcome the drawbacks of the current von Neumann computing architecture. Especially, spiking neural network (SNN) has received significant attention due to its ability to mimic the spike-driven behavior of biological neurons and synapses, potentially leading to low-power consumption and other advantages. In this work, we designed the indium-gallium-zinc oxide (IGZO) channel charge-trap flash (CTF) synaptic device based on a HfO2/Al2O3/Si3N4/Al2O3 layer. Our IGZO-based CTF device exhibits synaptic functions with 128 levels of synaptic weight states and spike-timing-dependent plasticity. The SNN-restricted Boltzmann machine was used to simulate the fabricated CTF device to evaluate the efficiency for the SNN system, achieving the high pattern-recognition accuracy of 83.9%. We believe that our results show the suitability of the fabricated IGZO CTF device as a synaptic device for neuromorphic computing.
RESUMO
In 2020, lilac trees showing virus-like symptoms such as leaf necrosis and chlorosis were observed in Korea. After RT-PCR detection with specific primer sets designed based on previously reported nucleotide sequences of viruses in lilac, the agent was identified as ligustrum virus A (LVA). The complete genome of the virus was sequenced and used for phylogenetic analysis. The genome of this novel strain of LVA, LVA-SNU, is 8524 nucleotides long, excluding the poly(A) tail, and shares the highest nucleotide sequence identity (77.28%) with LVA-Sob, which was detected in a plant of the same species, Syringa oblata, in China, whereas LVA-Sob shares higher sequence identity (97.89%) with LVA-SK, which has been detected in host plants of various species.
Assuntos
Anemia Hipocrômica , Ligustrum , Syringa , Vírus não Classificados , Vírus de DNA , Necrose , Filogenia , Folhas de PlantaRESUMO
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.
