# Efficient Calibration for RRAM-based In-Memory Computing using DoRA

Source: [Efficient Calibration for RRAM-based In-Memory Computing using DoRA](<https://www.sota2.com/research/paper/68fa77e21e209131cea75cdc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:28:27.917Z

Authors: Weirong Dong, Kai Zhou, Zhen Kong, Quan Cheng, Junkai Huang, Zhengke Yang, Masanori Hashimoto, Longyang Lin

Published: 2025-04-02T11:58:08.000Z

## Abstract

Resistive In-Memory Computing (RIMC) offers ultra-efficient computation for edge AI but faces accuracy degradation due to RRAM conductance drift over time. Traditional retraining methods are limited by RRAM's high energy consumption, write latency, and endurance constraints. We propose a DoRA-based calibration framework that restores accuracy by compensating influential weights with minimal calibration parameters stored in SRAM, leaving RRAM weights untouched. This eliminates in-field RRAM writes, ensuring energy-efficient, fast, and reliable calibration. Experiments on RIMC-based ResNet50 (ImageNet-1K) demonstrate 69.53% accuracy restoration using just 10 calibration samples while updating only 2.34% of parameters.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.03763>)

[PDF](<https://arxiv.org/pdf/2504.03763.pdf>)