Context-Aware Coding for Computing Memories

Abstract: Error correction and detection are the core components of all modern memory systems. Current computing memory systems use simple coding schemes to simultaneously meet the resiliency and latency requirements. In this talk, we review our recent results on context-aware coding for computing memories, an approach that explicitly takes into account various intrinsic side information for improved robustness to faults. We discuss both error correction and detection, codes’ theoretical properties, and provide examples of how these solutions can be implemented in practice. We explicitly describe the special case of the error localization codes. We also discuss promising future directions, connections with classical information theoretic concepts, and other applications. Joint work with C. Schoeny, M. Gottscho, I. Alam, and P. Gupta.

Biography:.  Lara Dolecek is a professor with the Electrical and Computer Engineering Department at UCLA. She received her BS, MS, and PhD degrees in EECS as well as an MA degree in Statistics, all from UC Berkeley. She is a recipient of several research and teaching awards including NSF CAREER, IBM Faculty Award, Intel Early Career Faculty Award, Okawa Research Grant, and UCLA Northrop Grumman Excellence in Teaching Award. With her research group and collaborators, she received numerous best paper awards. Her research interests include coding methods with applications to memories and storage, computing, quantum systems, and machine learning.

Host: Salman Avestmehr