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Hardware-Implemented DropConnect Function for Energy-Efficient Neuromorphic Computing
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DOI：10.1002/adfm.202503452.png)
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Abstract
En 中文
Achieving brain-level efficiency has long been the ultimate goal of computing. Considerable progress has been made in developing low-power neuromorphic building blocks to enable efficient neural network training. However, ultra-scalable nanodevices used for DropConnect regularization are demanded for full-hardware implementation of deep neural networks (DNNs). In this work, an energy-efficient ferroelectric synaptic transistor integrated with a threshold switch (TS) is presented, providing the proof-of-concept demonstration of hardware-implemented stochastic DropConnect function. The threshold switch enables stochastic dropout of synaptic weights by leveraging intrinsic variations that are difficult to eliminate, while unselected TS helps to minimize sneak-path currents. Compared to TS-free arrays, this approach reduces energy consumption by 31.7%. Further, two types of DNNs to show that the DropConnect can effectively combat the overfitting issue under different network architectures is explored. The research showcases a viable solution for the hardware implementation of scalable, resource-efficient DNNs.
Keywords:
artificial synapse
deep neural network
ferroelectric alpha-phase indium selenide
neuromorphic computing
threshold switch
Journal
IF:
19
Papers:
3.4W
Citations:
32.1W