In-Place Zero-Space Memory Protection for CNN
Authors: Hui Guan, Lin Ning, Zhen Lin, Xipeng Shen, Huiyang Zhou, Seung-Hwan Lim
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on VGG16, Res Net18, and Squeeze Net validate the effectiveness of the proposed solution. |
| Researcher Affiliation | Collaboration | Hui Guan1, Lin Ning1, Zhen Lin1, Xipeng Shen1, Huiyang Zhou1, Seung-Hwan Lim2 1North Carolina State University, Raleigh, NC, 27695 2Oak Ridge National Laboratory, Oak Ridge, TN 37831 {hguan2, lning, zlin4, xshen5, hzhou}@ncsu.edu, lims1@ornl.gov |
| Pseudocode | No | The paper describes algorithmic steps within the text and using mathematical formulations (e.g., in Section 4.1 'ADMM-based Training' and 'QAT with Throttling (QATT)'), but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | By default, We use the Image Net dataset [6] (ILSVRC 2012) for model training and evaluation. |
| Dataset Splits | No | The paper states using the Image Net dataset for model training and evaluation but does not specify the exact percentages or counts for training, validation, or test splits. It implicitly uses validation during WOT training (Figure 4) but no explicit split details are provided. |
| Hardware Specification | Yes | All the experiments are performed with Py Torch 1.0.1 on machines equipped with a 40-core 2.2GHz Intel Xeon Silver 4114 processor, 128GB of RAM, and an NVIDIA TITAN Xp GPU with 12GB memory. |
| Software Dependencies | Yes | All the experiments are performed with Py Torch 1.0.1... Distiller [32] is used for 8-bit quantization. The CUDA version is 10.1. |
| Experiment Setup | Yes | We set λ to 0.0001 for all of the CNNs. Model training uses stochastic gradient descent with a constant learning rate 0.0001 and momentum 0.9. Batch size is 32 for VGG16_bn and Res Net152, 64 for Res Net50 and VGG16, and 128 for the remaining models. Training stops as long as the model accuracy after weight throttling reaches its 8-bit quantized version. |