chore: fix cifar runtime

README.md

@@ -158,7 +158,7 @@ Various tutorials are given for [built-in models](docs/built-in-models/ml_exampl
 
 - [Sentiment analysis with transformers](use_case_examples/sentiment_analysis_with_transformer): predict if an encrypted tweet / short message is positive, negative or neutral, using FHE. The [live interactive](https://huggingface.co/spaces/zama-fhe/encrypted_sentiment_analysis) demo is available on Hugging Face. This [blog post](https://huggingface.co/blog/sentiment-analysis-fhe) explains how this demo works!
 
-- [CIFAR10 FHE-friendly model with Brevitas](use_case_examples/cifar/cifar_brevitas_training): train a VGG9 FHE-compatible neural network using Brevitas, and a script to run the neural network in FHE. Execution in FHE takes ~20 minutes per image and shows an accuracy of 88.7%.
+- [CIFAR10 FHE-friendly model with Brevitas](use_case_examples/cifar/cifar_brevitas_training): train a VGG9 FHE-compatible neural network using Brevitas, and a script to run the neural network in FHE. Execution in FHE takes ~4 minutes per image and shows an accuracy of 88.7%.
 
 - [CIFAR10 / CIFAR100 FHE-friendly models with Transfer Learning approach](use_case_examples/cifar/cifar_brevitas_finetuning): series of three notebooks, that convert a pre-trained FP32 VGG11 neural network into a quantized model using Brevitas. The model is fine-tuned on the CIFAR data-sets, converted for FHE execution with Concrete ML and evaluated using FHE simulation. For CIFAR10 and CIFAR100, respectively, our simulations show an accuracy of 90.2% and 68.2%.
 

use_case_examples/cifar/cifar_brevitas_training/README.md

@@ -98,7 +98,7 @@ Experiments were conducted on an m6i.metal machine offering 128 CPU cores and 51
 | VGG FHE (simulation\*) | 6 bits   | 86.0     |
 | VGG FHE                | 6 bits   | 86.0\*\* |
 
-We ran the FHE inference over 10 examples and achieved 100% similar predictions between the simulation and FHE. The overall accuracy for the entire data-set is expected to match the simulation. The original model with a maximum of 13 bits of precision ran in around 9 hours on the specified hardware. Using the rounding approach, the final model ran in **31 minutes**, providing a speedup factor of 18x while preserving accuracy. This significant performance improvement demonstrates the benefits of the rounding operator in the FHE setting.
+We ran the FHE inference over 10 examples and achieved 100% similar predictions between the simulation and FHE. The overall accuracy for the entire data-set is expected to match the simulation. The original model (no rounding) with a maximum of 13 bits of precision runs in around 9 hours on the specified hardware. Using the rounding approach, the final model ran in **4 minutes**. This significant performance improvement demonstrates the benefits of the rounding operator in the FHE setting.
 
 \* Simulation is used to evaluate the accuracy in the clear for faster debugging.
 \*\* We ran the FHE inference over 10 examples and got 100% similar predictions between the simulation and FHE. The overall accuracy for the entire data-set is expected to match the simulation.