CryptoNAS: Private Inference on a ReLU Budget
Authors: Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Crypto NAS improves accuracy by 3.4% and latency by 2.4 over the state-of-the-art. Crypto NAS is evaluated on CIFAR-10 and CIFAR-100 [18]. We see that Crypto NAS s Pareto frontier dominates all prior points for regions of interest. |
| Researcher Affiliation | Academia | Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen, Siddharth Garg New York University {zg451, akv275, bjr5, sg175}@nyu.edu |
| Pseudocode | Yes | Algorithm 1 Crypto NAS Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology or a link to a repository. |
| Open Datasets | Yes | Crypto NAS is evaluated on CIFAR-10 and CIFAR-100 [18]. |
| Dataset Splits | No | The paper mentions using a 'validation set' in Algorithm 1, but does not provide specific percentages or sample counts for the training, validation, and test splits needed for reproduction. It only states that 'Datasets are preprocessed with image centering (subtracting mean and dividing standard deviation), and images are augmented for training using random horizontal flips, 4 pixel padding, and taking random crops.' |
| Hardware Specification | Yes | Experiments for latency are run on a 3 GHz Intel Xeon E5-2690 processor with 60GB of RAM, and networks are trained on Tesla P100 GPUs. |
| Software Dependencies | No | The paper mentions using the SEAL [16] library and the ABY library [17], but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Datasets are preprocessed with image centering (subtracting mean and dividing standard deviation), and images are augmented for training using random horizontal flips, 4 pixel padding, and taking random crops. We use Crypto NAS to discover three models with depth={6, 12, 24}, which we refer to as CNet1, CNet2 and CNet3. |