ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems
Authors: Mahdi Soleymani, Ramy E. Ali, Hessam Mahdavifar, A. Salman Avestimehr8342-8350
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments on a large number of datasets and model architectures show significant degraded mode accuracy improvement by up to 58% over Par M. |
| Researcher Affiliation | Academia | 1 University of Michigan Ann Arbor 2 University of Southern California (USC) |
| Pseudocode | Yes | Algorithm 1: Error-locator algorithm. Input: xi s, yi s for i Aavl, E and K. Output: Error locations. Step 1: Find polynomials P(x) def = K+E 1 P i=0 P ixi and Q(x) def = K+E 1 P i=0 Qixi by solving the following system of linear equations P(xi) = yi Q(xi), i Aavl. |
| Open Source Code | No | The paper provides links to the code for a baseline method (Par M) and to pretrained models, but not to the source code for Approx IFER itself. 'The pretrained models are available at https://github.com/huyvnphan/Py Torch_CIFAR10.' and 'The results of Par M are obtained using the codes available at https://github.com/thesys-lab/parity-models.' |
| Open Datasets | Yes | We run experiments on MNIST (Le Cun et al. 1998), Fashion-MNIST (Xiao, Rasul, and Vollgraf 2017), CIFAR (Krizhevsky, Hinton et al. 2009) and Image Net (Deng et al. 2009) datasets. |
| Dataset Splits | No | The paper mentions using 'the test dataset' for evaluation but does not specify explicit training or validation splits, nor does it provide percentages or sample counts for any data partitioning. |
| Hardware Specification | Yes | The latency evaluation experiments are written with MPI4py (Dalcin et al. 2011) and performed on Amazon AWS c5.xlarge instances. |
| Software Dependencies | No | The paper mentions software like 'Py Torch (Paszke et al. 2019)' and 'MPI4py (Dalcin et al. 2011)' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | No | The paper does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or detailed training configurations in the main text for experiment reproduction. |