Dynamic Memory Error Model Estimation for Read and ECC Adaptations
Abstract: One of the challenges introduced by NVM scaling and 3D stacking is maintaining process uniformity, leading to increased variability between memory dies, blocks and pages and across different operational conditions. This requires using an adaptive memory system. In this work, we describe methods for online memory error model estimation, which can be used for adapting various system parameters, such as ECC or read parameters, to the specific memory page and the specific memory conditions.
Bio: Eran Sharon is an Engineering Fellow at WD, heading an R&D team developing a broad range of coding, DSP and memory management solutions for NVM. Eran has numerous publications in leading venues and holds over 130 issued patents in the fields of storage and communications. He received his PhD in EE (2009) from Tel-Aviv University. He is the recipient of several awards, including Weinstein excellence prize, ACC Feder Prize for best graduate student research and several SanDisk Innovation awards.