update README.md

Signed-off-by: Lei Hsiung <[email protected]>

README.md

@@ -1,25 +1,29 @@
 ---
 title: NeuralFuse
-emoji: 🧠
+emoji: ⚡
 colorFrom: yellow
 colorTo: indigo
 sdk: static
 pinned: false
 ---
 
-# Nerfies
+# NeuralFuse
 
-This is the repository that contains source code for the [Nerfies website](https://nerfies.github.io).
+Official project page of the paper "[NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes](https://arxiv.org/abs/2306.16869)."
 
-If you find Nerfies useful for your work please cite:
-```
-@article{park2021nerfies
-  author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
-  title     = {Nerfies: Deformable Neural Radiance Fields},
-  journal   = {ICCV},
-  year      = {2021},
-}
-```
+![NeuralFuse](static/images/teaser.png)
 
-# Website License
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains problematically high. An effective strategy for reducing such consumption is supply-voltage reduction, but if done too aggressively, it can lead to accuracy degradation. This is due to random bit-flips in static random access memory (SRAM), where model parameters are stored. To address this challenge, we have developed NeuralFuse, a novel add-on module that handles the energy-accuracy tradeoff in low-voltage regimes by learning input transformations and using them to generate error-resistant data representations, thereby protecting DNN accuracy in both nominal and low-voltage scenarios. As well as being easy to implement, NeuralFuse can be readily applied to DNNs with limited access, such cloud-based APIs that are accessed remotely or non-configurable hardware. Our experimental results demonstrate that, at a 1% bit-error rate, NeuralFuse can reduce SRAM access energy by up to 24% while recovering accuracy by up to 57%. To the best of our knowledge, this is the first approach to addressing low-voltage-induced bit errors that requires no model retraining.
+
+
+## Citation
+If you find this helpful for your research, please cite our paper as follows:
+
+    @article{sun2024neuralfuse,
+      title={{NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes}},
+      author={Hao-Lun Sun and Lei Hsiung and Nandhini Chandramoorthy and Pin-Yu Chen and Tsung-Yi Ho},
+      booktitle = {Advances in Neural Information Processing Systems},
+      publisher = {Curran Associates, Inc.},
+      volume = {37},
+      year = {2024}
+     }