D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, The missing memristor found, Nature, vol.453, issue.7191, p.80, 2008.

N. R. Shanbhag, N. Verma, Y. Kim, A. D. Patil, and L. R. Varshney, Shannon-inspired statistical computing for the nanoscale era, Proceedings of the IEEE, vol.107, pp.90-107, 2018.

S. Jain, A. Ankit, I. Chakraborty, T. Gokmen, M. Rasch et al., Neural network accelerator design with resistive crossbars: Opportunities and challenges, IBM J. Res. Dev, vol.63, issue.6, p.10, 2019.

R. Gharpinde, P. L. Thangkhiew, K. Datta, and I. Sengupta, A scalable in-memory logic synthesis approach using memristor crossbar, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol.26, issue.2, pp.355-366, 2017.

A. Ankit, I. E. Hajj, S. R. Chalamalasetti, G. Ndu, M. Foltin et al., PUMA: A programmable ultra-efficient memristor-based accelerator for machine learning inference, Proc. 24th Int. Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS '19), pp.715-731, 2019.

Y. Cassuto and K. Crammer, In-memory Hamming similarity computation in resistive arrays, 2015 IEEE International Symposium on Information Theory (ISIT), pp.819-823, 2015.

Z. Chen, C. Schoeny, and L. Dolecek, Hamming distance computation in unreliable resistive memory, IEEE Transactions on Communications, vol.66, issue.11, pp.5013-5027, 2018.

R. M. Roth, Fault-tolerant dot-product engines, IEEE Transactions on Information Theory, vol.65, issue.4, pp.2046-2057, 2018.

S. Liu, Y. Wang, M. Fardad, and P. K. Varshney, A memristor-based optimization framework for artificial intelligence applications, IEEE Circuits and Systems Magazine, vol.18, issue.1, pp.29-44, 2018.

P. M. Sheridan, F. Cai, C. Du, W. Ma, Z. Zhang et al., Sparse coding with memristor networks, Nature Nanotechnology, vol.12, issue.8, p.784, 2017.

Y. Jeong, J. Lee, J. Moon, J. H. Shin, and W. D. Lu, K-means data clustering with memristor networks, Nano letters, vol.18, issue.7, pp.4447-4453, 2018.

I. Nahlus, E. P. Kim, N. R. Shanbhag, and D. Blaauw, Energy-efficient dot product computation using a switched analog circuit architecture, Proc. 2014 Int. Symp. Low Power Electronics and Design (ISLPED '14), pp.315-318, 2014.

N. C. Wang, S. K. Gonugondla, I. Nahlus, N. R. Shanbhag, and E. Pop, GDOT: A graphene-based nanofunction for dot-product computation, Proc. 2016 IEEE Symp. VLSI Technology, 2016.

S. Dutta, V. Cadambe, and P. Grover, Short-dot: Computing large linear transforms distributedly using coded short dot products, Advances in Neural Information Processing Systems, vol.29, pp.2100-2108, 2016.

S. Chung, T. J. Richardson, and R. L. Urbanke, Analysis of sumproduct decoding of low-density parity-check codes using a Gaussian approximation, IEEE Trans. Information Theory, vol.47, issue.2, pp.657-670, 2001.

J. Bardet, P. Doukhan, G. Lang, and N. Ragache, Dependent lindeberg central limit theorem and some applications, ESAIM: Probability and Statistics, vol.12, pp.154-172, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00127903

R. J. Serfling, Approximation Theorems of Mathematical Statistics, vol.162, 2009.

N. S. Pillai and X. Meng, An unexpected encounter with cauchy and lévy, The Annals of Statistics, vol.44, issue.5, pp.2089-2097, 2016.

H. Seltman, Approximations for mean and variance of a ratio, unpublished note, 2012.

Z. Ye, S. H. Wu, and T. Prodromakis, Computing shortest paths in 2d and 3d memristive networks, pp.537-552, 2014.