ArticleScience advances2023
Reconfigurable neuromorphic computing block through integration of flash synapse arrays and super-steep neurons.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Multimodal In-Sensor Computing with Dual-Phase Organic Synapses for Wearable Fitness Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Hybrid functional 3D artificial synapses for convolution and reinforcement learning.Science advances · 2025Article
- In situ observation of oxygen ion dynamics in topological phase change memristors through self-assembled interface design.Science advances · 2025Article
- Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware.Nature communications · 2024Article
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Authors and funding
10 authors.
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Abstract
Neuromorphic computing (NC) architecture inspired by biological nervous systems has been actively studied to overcome the limitations of conventional von Neumann architectures. In this work, we propose a reconfigurable NC block using a flash-type synapse array, emerging positive feedback (PF) neuron devices, and CMOS peripheral circuits, and integrate them on the same substrate to experimentally demonstrate the operations of the proposed NC block. Conductance modulation in the flash memory enables the NC block to be easily calibrated for output signals. In addition, the proposed NC block uses a reduced number of devices for analog-to-digital conversions due to the super-steep switching characteristics of the PF neuron device, substantially reducing the area overhead of NC block. Our NC block shows high energy efficiency (37.9 TOPS/W) with high accuracy for CIFAR-10 image classification (91.80%), outperforming prior works. This work shows the high engineering potential of integrating synapses and neurons in terms of system efficiency and high performance.
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