Eran Sharon | Distinguished Engineer
Sandisk

Future of Memory and Storage - Day 2 @ 15:35

AI driven Read Thresholds

In high-density QLC deployments, maintaining optimal read thresholds becomes increasingly challenging as NAND flash technology scales to higher densities and 3D architectures, due to QLC’s narrow voltage margins and heightened sensitivity to temperature variation and wear-induced drift.  Traditional static threshold schemes fail to adapt to dynamic conditions such as temperature variations and wear-induced shifts. This work introduces Adaptive Read Thresholds (ART), an AI-driven solution leveraging compact machine learning models to predict optimal thresholds in real time. ART combines offline training with online inference using Gradient Boosting Trees, leveraging binary symmetrical tree models, achieving tens of nanosecond-scale latency per prediction. Large-scale experiments on SanDisk SSDs demonstrate significant reductions in bit error rates and improved endurance compared to legacy Table & Tagging methods. ART’s lightweight architecture enables ASIC integration, paving the way for state of the art next generation QLC storage systems with enhanced reliability and performance, enabling intelligent storage with real time inference.

Eran Sharon, Distinguished Engineer, Sandisk

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Eran Sharon / AI driven Read Thresholds

Eran Sharon, Distinguished Engineer,
Sandisk

Eran Sharon is a Distinguished Engineer at Sandisk, managing the Advanced Algorithms & Research team, developing Error Correction Coding, Signal Processing, ML/AI, and Cryptographic solutions for Storage. Eran received his PhD in EE (2009) from Tel-Aviv University. He has 50+ academic...

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