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Dendritic Computing with Multigate Ferroelectric Field-Effect Transistors
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AI
DOI：10.1021/acs.nanolett.5c03241.png)
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Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer computational complexity far less than that of their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local nonlinear accumulation - playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multigate ferroelectric field-effect transistor that mimics dendrites. It leverages ferroelectric nonlinearity for local computations within dendritic branches while utilizing the transistor action to generate the neuronal output. The branched architecture enables smaller crossbar arrays in hardware integration, improving efficiency. Using an experimentally calibrated device-circuit-algorithm cosimulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance compared to much larger networks without dendrites (similar to 17x fewer trainable weight parameters). These findings suggest that dendritic hardware can significantly improve computational efficiency and learning capacity of neuromorphic systems optimized for edge applications.
Keyword:
Ferroelectric field-effect transistor
Brain-inspiredcomputing
Dendrites
Hardware-software codesign
Edge artificial intelligence
期刊
IF:
9.1
论文数:
2.7W
被引数:
16.5W
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Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning树突为人工神经网络赋予精确、鲁棒且参数高效的学习能力。.png)
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