Quantizing for Noisy Flash Memory Channels

TL;DR

This paper proposes a joint optimization framework for quantization and verify levels in flash memory-based PIM systems, significantly enhancing reliability by minimizing MSE under noisy channel conditions.

Contribution

It introduces an iterative algorithm for joint optimization of quantization and verify levels tailored for flash-based PIM, addressing their unique reliability challenges.

Findings

Improved image quality metrics (MSE, PSNR) in experiments.

Enhanced reliability of flash-based PIM systems.

Significant reduction in quantization errors.

Abstract

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.