用铁电晶体管模拟树突结构，提升神经网络效率。
Dendritic Computing with Multi-Gate Ferroelectric Field-Effect Transistors
- 用多栅铁电场效应管实现树突局部非线性计算。
- 仅需原模型1/17参数量就达到更好性能。
- 适合边缘计算场景的高效类脑硬件设计。
尽管受大脑神经元系统启发，人工神经网络通常采用点状神经元，其计算复杂度远低于生物神经元。真实神经元具有树突分支，可连接不同突触并实现局部非线性累积，对信息处理与学习至关重要。受此启发，本文提出一种基于多栅铁电场效应晶体管的新神经元设计，模拟树突功能：利用铁电非线性在树突分支内进行局部计算，同时通过晶体管行为生成最终输出。分支结构使硬件集成时可使用更小的交叉阵列，提升效率。通过实验校准的器件-电路-算法协同仿真框架验证，采用该树突神经元的网络在性能上优于无树突的大规模网络（训练参数减少约17倍）。结果表明，树突型硬件可显著提升类脑系统在边缘应用中的计算效率与学习能力。
原文摘要 · Abstract (English)
Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer far less computational complexity than their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local non-linear accumulation - playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multi-gate 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 final neuronal output. The branched architecture paves the way for utilizing smaller crossbar arrays in hardware integration, leading to greater efficiency. Using an experimentally calibrated device-circuit-algorithm co-simulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance in comparison to much larger networks without dendrites ($\sim$17$\times$ 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.
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