Thermal Noise Measurement and Characterization for Modern Semiconductor Devices

Chen, Chih‐Hung

doi:10.1109/mim.2021.9400958

Cited by 8 publications

(1 citation statement)

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“…An obvious benefit is that the intrinsic noise in devices waives the necessity to design complex circuitry specialized for Gaussian noise generation using Zener diodes or other devices [28]. Various types of noise exist in semiconductor devices, such as thermal noise [29], random telegraph noise [30], 1/f noise [31], etc [32,33], but a comprehensive study on the overfitting suppression effect of noise, at least for one or two types of noise, is still missing.…”

Section: Introductionmentioning

confidence: 99%

Synaptic 1/f noise injection for overfitting suppression in hardware neural networks

Shao

Chai

et al. 2022

Neuromorph. Comput. Eng.

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Overfitting is a common and critical challenge for neural networks trained with limited dataset. The conventional solution is software-based regularization algorithms such as Gaussian noise injection. Semiconductor noise, such as 1/f noise, in artificial neuron/synapse devices, which is often regarded as undesirable disturbance to the hardware neural networks (HNNs), could also play a useful role in suppressing overfitting, but that is as yet unexplored. In this work, we proposed the idea of using 1/f noise injection to suppress overfitting in different neural networks, and demonstrated that: (i) 1/f noise could suppress the overfitting in Multilayer Perceptron (MLP) and long short-term memory (LSTM); (ii) 1/f noise and Gaussian noise performs similarly for the MLP but differently for the LSTM; (iii) The superior performance of 1/f noise on LSTM can be attributed to its intrinsic long range dependence. This work reveals that 1/f noise, which is common in semiconductor devices, can be a useful solution to suppress the overfitting in HNNs, and more importantly, further evidents that the imperfectness of semiconductor devices is a rich mine of solutions to boost the development of brain-inspired hardware technologies in the AI era.

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