# Quantizing for Noisy Flash Memory Channels

arXiv: https://arxiv.org/abs/2506.17646 · HTML: https://mlsanity.com/paper/2506.17646/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 21 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Error Characterization, Mitigation, and Recovery in Flash Memory Based Solid-State Drives](https://mlsanity.com/paper/1706.08642/) (2017)

## Abstract (arXiv)

Flash memory-based processing-in-memory (flash-based PIM) offers high storage capacity and computational efficiency but faces significant reliability challenges due to noise in high-density multi-level cell (MLC) flash memories. Existing verify level optimization methods are designed for general storage scenarios and fail to address the unique requirements of flash-based PIM systems, where metrics such as mean squared error (MSE) and peak signal-to-noise ratio (PSNR) are critical. This paper introduces an integrated framework that jointly optimizes quantization and verify levels to minimize the MSE, considering both quantization and flash memory channel errors. We develop an iterative algorithm to solve the joint optimization problem. Experimental results on quantized images and SwinIR model parameters stored in flash memory show that the proposed method significantly improves the reliability of flash-based PIM systems.