Benchmarking Inference Performance of Deep Learning Models on Analog
Devices
- URL: http://arxiv.org/abs/2011.11840v2
- Date: Wed, 16 Dec 2020 22:04:52 GMT
- Title: Benchmarking Inference Performance of Deep Learning Models on Analog
Devices
- Authors: Omobayode Fagbohungbe, Lijun Qian
- Abstract summary: It is observed that deeper models and models with more redundancy in design such as VGG are more robust to the noise in general.
The performance is also affected by the design philosophy of the model, the detailed structure of the model, the exact machine learning task, as well as the datasets.
- Score: 3.520496620951778
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Analog hardware implemented deep learning models are promising for
computation and energy constrained systems such as edge computing devices.
However, the analog nature of the device and the associated many noise sources
will cause changes to the value of the weights in the trained deep learning
models deployed on such devices. In this study, systematic evaluation of the
inference performance of trained popular deep learning models for image
classification deployed on analog devices has been carried out, where additive
white Gaussian noise has been added to the weights of the trained models during
inference. It is observed that deeper models and models with more redundancy in
design such as VGG are more robust to the noise in general. However, the
performance is also affected by the design philosophy of the model, the
detailed structure of the model, the exact machine learning task, as well as
the datasets.
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