2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | M.9.4 | ||
| Paper Title | Noisy In-Memory Recursive Computation with Memristor Crossbars | ||
| Authors | Elsa Dupraz, IMT Atlantique, France; Lav Varshney, University of Illinois at Urbana-Champaign, United States | ||
| Session | M.9: Topics in Coding for Storage and Memories | ||
| Presentation | Lecture | ||
| Track | Coding for Storage and Memories | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | This paper considers iterative dot-product computation implemented on in-memory memristor crossbar substrates. To address the case where true memristor conductance values may differ from their target values, it introduces a theoretical framework that characterizes the effect of conductance value variations on the final computation. For simple dot-products, the final computation error can be approximated by a Gaussian distribution; the mean and variance values of the corresponding Gaussian distribution are provided. For iterative dot-product computation, recursive expressions are derived for the means and variances of the successive computation outputs. Experiments verify the accuracy of the proposed analysis on both synthetic data and on images processed with memristor-based principal component analysis. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
