Add paper source and generated artifacts

- Add LaTeX source files (main.tex, sections, bibliography)
- Add generated tables (12 .tex files from analysis pipeline)
- Add generated figures (8 PDFs and PNGs from analysis pipeline)
- Add compiled paper PDF (28 pages, 552 KB)
- Add Makefile for paper compilation
- Tables and figures prove programmatic reproducibility

Author: dariocazzani

paper/Makefile

@@ -0,0 +1,13 @@
+MAIN = main
+LATEX = pdflatex
+
+all: $(MAIN).pdf
+
+$(MAIN).pdf: $(MAIN).tex preamble.tex macros.tex bibliography.tex
+	$(LATEX) $(MAIN)
+	$(LATEX) $(MAIN)
+
+clean:
+	rm -f *.aux *.bbl *.blg *.log *.out *.toc *.lof *.lot *.fls *.fdb_latexmk *.synctex.gz
+
+.PHONY: all clean

paper/bibliography.tex

@@ -0,0 +1,199 @@
+% Shared bibliography — all venues use the same references
+
+\bibliographystyle{plain}
+% \bibliography{references}  % Uncomment when references.bib exists
+
+% Temporary inline references for skeleton
+\begin{thebibliography}{99}
+
+\bibitem{wang2023bitnet}
+H.~Wang et al.
+\newblock BitNet: Scaling 1-bit Transformers for Large Language Models.
+\newblock \emph{arXiv:2310.11453}, 2023.
+
+\bibitem{ma2024bitnetb158}
+S.~Ma et al.
+\newblock The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.
+\newblock \emph{arXiv:2402.17764}, 2024.
+
+\bibitem{courbariaux2015binaryconnect}
+M.~Courbariaux et al.
+\newblock BinaryConnect: Training Deep Neural Networks with Binary Weights during Propagations.
+\newblock \emph{NeurIPS}, 2015.
+
+\bibitem{rastegari2016xnornet}
+M.~Rastegari et al.
+\newblock XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.
+\newblock \emph{ECCV}, 2016.
+
+\bibitem{li2016ternary}
+F.~Li et al.
+\newblock Ternary Weight Networks.
+\newblock \emph{arXiv:1605.04711}, 2016.
+
+\bibitem{krizhevsky2009cifar}
+A.~Krizhevsky.
+\newblock Learning Multiple Layers of Features from Tiny Images.
+\newblock Technical report, 2009.
+
+\bibitem{devries2017cutout}
+T.~DeVries and G.~Taylor.
+\newblock Improved Regularization of Convolutional Neural Networks with Cutout.
+\newblock \emph{arXiv:1708.04552}, 2017.
+
+\bibitem{cubuk2019autoaugment}
+E.~Cubuk et al.
+\newblock AutoAugment: Learning Augmentation Strategies from Data.
+\newblock \emph{CVPR}, 2019.
+
+\bibitem{cubuk2020randaugment}
+E.~Cubuk et al.
+\newblock RandAugment: Practical Automated Data Augmentation with a Reduced Search Space.
+\newblock \emph{NeurIPS}, 2020.
+
+\bibitem{he2016resnet}
+K.~He et al.
+\newblock Deep Residual Learning for Image Recognition.
+\newblock \emph{CVPR}, 2016.
+
+\bibitem{hinton2015distilling}
+G.~Hinton, O.~Vinyals, and J.~Dean.
+\newblock Distilling the Knowledge in a Neural Network.
+\newblock \emph{NeurIPS Deep Learning Workshop}, 2015.
+
+\bibitem{zhu2017ttq}
+C.~Zhu, S.~Han, H.~Mao, and W.~Dally.
+\newblock Trained Ternary Quantization.
+\newblock \emph{ICLR}, 2017.
+
+\bibitem{kim2019qkd}
+J.~Kim, Y.~Bhalgat, J.~Lee, C.~Patel, and N.~Kwak.
+\newblock QKD: Quantization-aware Knowledge Distillation.
+\newblock \emph{arXiv:1911.12491}, 2019.
+
+\bibitem{wang2019haq}
+K.~Wang, Z.~Liu, Y.~Lin, J.~Lin, and S.~Han.
+\newblock HAQ: Hardware-Aware Automated Quantization with Mixed Precision.
+\newblock \emph{CVPR}, 2019.
+
+\bibitem{nielsen2024bitnetreloaded}
+J.~Nielsen and P.~Schneider-Kamp.
+\newblock BitNet b1.58 Reloaded: State-of-the-art Performance Also on Smaller Networks.
+\newblock \emph{arXiv:2407.09527}, 2024.
+
+\bibitem{kim2025bdnet}
+D.~Kim, J.-S.~Lee, N.-r.~Kim, S.~Lee, and J.-H.~Lee.
+\newblock BD-Net: Has Depth-Wise Convolution Ever Been Applied in Binary Neural Networks?
+\newblock \emph{AAAI}, 2026.
+
+\bibitem{zhou2016dorefa}
+S.~Zhou, Y.~Wu, Z.~Ni, X.~Zhou, H.~Wen, and Y.~Zou.
+\newblock DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.
+\newblock \emph{arXiv:1606.06160}, 2016.
+
+\bibitem{dong2019hawq}
+Z.~Dong, Z.~Yao, A.~Gholami, M.~Mahoney, and K.~Keutzer.
+\newblock HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision.
+\newblock \emph{ICCV}, 2019.
+
+\bibitem{elthakeb2020dcq}
+A.~T.~Elthakeb, P.~Pilligundla, F.~Mireshghallah, A.~Alaghi, and H.~Esmaeilzadeh.
+\newblock Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks.
+\newblock \emph{arXiv:1906.06033}, 2020.
+
+\bibitem{le2015tinyimagenet}
+Y.~Le and X.~Yang.
+\newblock Tiny ImageNet Visual Recognition Challenge.
+\newblock CS 231N, Stanford University, 2015.
+
+\bibitem{liu2020reactnet}
+Z.~Liu, Z.~Shen, M.~Savvides, and K.-T.~Cheng.
+\newblock ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions.
+\newblock \emph{ECCV}, 2020.
+
+\bibitem{guo2022bnext}
+N.~Guo, J.~Bethge, C.~Meinel, and H.~Yang.
+\newblock BNext: Any Precision Binary Neural Network.
+\newblock \emph{arXiv:2211.12933}, 2022.
+
+\bibitem{dong2020hawqv2}
+Z.~Dong, Z.~Yao, D.~Arfeen, A.~Gholami, M.~W.~Mahoney, and K.~Keutzer.
+\newblock HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks.
+\newblock \emph{NeurIPS}, 2020.
+
+\bibitem{qin2020irnet}
+H.~Qin, R.~Gong, X.~Liu, M.~Shen, Z.~Wei, F.~Yu, and J.~Song.
+\newblock Forward and Backward Information Retention for Accurate Binary Neural Networks.
+\newblock \emph{CVPR}, 2020.
+
+\bibitem{zhao2024sqakd}
+K.~Zhao and M.~Zhao.
+\newblock SQAKD: Self-supervised Quantization-Aware Knowledge Distillation.
+\newblock \emph{AISTATS}, 2024.
+
+\bibitem{bengio2013estimating}
+Y.~Bengio, N.~Léonard, and A.~Courville.
+\newblock Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.
+\newblock \emph{arXiv:1308.3432}, 2013.
+
+\bibitem{kim2021relaxloss}
+J.~Kim, S.~Bhattacharjee, S.~Park, S.~Jung, and Y.~M.~Kim.
+\newblock RelaxLoss: Penalty-Free Quantization through Normalized Loss.
+\newblock \emph{arXiv:2105.00944}, 2021.
+
+\bibitem{zhang2018mixup}
+H.~Zhang, M.~Cisse, Y.~N.~Dauphin, and D.~Lopez-Paz.
+\newblock mixup: Beyond Empirical Risk Minimization.
+\newblock \emph{ICLR}, 2018.
+
+\bibitem{gundersen2018reproducibility}
+O.~E.~Gundersen and S.~Kjensmo.
+\newblock State of the Art: Reproducibility in Artificial Intelligence.
+\newblock \emph{AAAI}, 2018.
+
+\bibitem{hutson2018ai}
+M.~Hutson.
+\newblock Artificial Intelligence Faces Reproducibility Crisis.
+\newblock \emph{Science}, 359(6377):725--726, 2018.
+
+\bibitem{dodge2019mlchecklist}
+J.~Dodge, S.~Gururangan, D.~Card, R.~Schwartz, and N.~A.~Smith.
+\newblock Show Your Work: Improved Reporting of Experimental Results.
+\newblock \emph{EMNLP}, 2019.
+
+\bibitem{tflite}
+TensorFlow Lite Team.
+\newblock TensorFlow Lite: Deploy machine learning models on mobile and edge devices.
+\newblock \url{https://www.tensorflow.org/lite}, 2023.
+
+\bibitem{pytorch_mobile}
+PyTorch Team.
+\newblock PyTorch Mobile: End-to-end workflow from training to deployment.
+\newblock \url{https://pytorch.org/mobile/}, 2023.
+
+\bibitem{bitblas}
+L.~Wang, L.~Lei, L.~Ye, Y.~Zhao, W.~Chen, D.~Lin, X.~Zheng, and C.~Yu.
+\newblock BitBLAS: A High-Performance Library for Quantized Deep Learning.
+\newblock \emph{arXiv:2410.16144}, 2024.
+
+\bibitem{pytorch_cifar_kuangliu}
+K.~Liu.
+\newblock Train CIFAR10 with PyTorch.
+\newblock \url{https://github.com/kuangliu/pytorch-cifar}, 2017.
+
+\bibitem{pytorch_cifar100_weiaicunzai}
+W.~Zhang.
+\newblock PyTorch CIFAR-100 Benchmark.
+\newblock \url{https://github.com/weiaicunzai/pytorch-cifar100}, 2019.
+
+\bibitem{tiny_imagenet_benchmark}
+Y.~Le and X.~Yang.
+\newblock Tiny ImageNet Visual Recognition Challenge.
+\newblock CS 231N, Stanford University, 2015.
+
+\bibitem{cover2006information}
+T.~M.~Cover and J.~A.~Thomas.
+\newblock Elements of Information Theory, 2nd Edition.
+\newblock Wiley-Interscience, 2006.
+
+\end{thebibliography}