AutoRRAM: An Agile RRAM Compiler Featuring Simultaneous Layout/Netlist Generation and Cross-Technology Migration

As a next-generation non-volatile memory technology, Resistive Random Access Memory (RRAM) has garnered significant attention due to its advantages in high-speed read/write operations, high-density integration, and low power consumption. However, conventional design methodologies struggle to meet the rapid iteration demands of RRAM IP cores. To address this challenge, we propose AutoRRAM, an agile development framework-based compiler that enables automated design of RRAM memory arrays and peripheral circuits (including decoders and multiplexers) through parametric modeling, supporting automatic generation of physical layouts. As the first Python-based portable development platform in this domain, AutoRRAM innovatively achieves process migration across different technologies from 40nm to 55nm and 28nm, while also supporting dynamic switching between memory storage and in-memory computing modes. Experimental results demonstrate that compared with traditional full-custom design approaches, AutoRRAM reduces the design cycle from several months to less than an hour while maintaining comparable circuit and area performance. This compiler, which combines high efficiency with scalability, not only provides a standardized design framework for RRAM technology research but also facilitates the industrial application process of RRAM IP cores through its user-friendly development paradigm.

Authors

Institutions

Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1109/iseda65950.2025.11101707
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AutoRRAM: An Agile RRAM Compiler Featuring Simultaneous Layout/Netlist Generation and Cross-Technology Migration

Liang Zhao, Zhichao Lu, Jinglei Hao, Zezhi Chen et al.
Advanced Memory and Neural Computing
article

AutoRRAM: An Agile RRAM Compiler Featuring Simultaneous Layout/Netlist Generation and Cross-Technology Migration

Liang Zhao, Zhichao Lu, Jinglei Hao, Zezhi Chen, Qian Qin, Yukai Lu, Chenkai Chai, Tianze Wu, Zuochang Ye
article en

Abstract

As a next-generation non-volatile memory technology, Resistive Random Access Memory (RRAM) has garnered significant attention due to its advantages in high-speed read/write operations, high-density integration, and low power consumption. However, conventional design methodologies struggle to meet the rapid iteration demands of RRAM IP cores. To address this challenge, we propose AutoRRAM, an agile development framework-based compiler that enables automated design of RRAM memory arrays and peripheral circuits (including decoders and multiplexers) through parametric modeling, supporting automatic generation of physical layouts. As the first Python-based portable development platform in this domain, AutoRRAM innovatively achieves process migration across different technologies from 40nm to 55nm and 28nm, while also supporting dynamic switching between memory storage and in-memory computing modes. Experimental results demonstrate that compared with traditional full-custom design approaches, AutoRRAM reduces the design cycle from several months to less than an hour while maintaining comparable circuit and area performance. This compiler, which combines high efficiency with scalability, not only provides a standardized design framework for RRAM technology research but also facilitates the industrial application process of RRAM IP cores through its user-friendly development paradigm.

Zhejiang University (CN), Tsinghua University (CN)
Ministry of Science and Technology, National Natural Science Foundation of China
Reduced inequalities
Openalex Percentile: Top 81%
Advanced Memory and Neural Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

AutoRRAM: An Agile RRAM Compiler Featuring Simultaneous Layout/Netlist Generation and Cross-Technology Migration — Liang Zhao, Zhichao Lu, et al. · (2025) | TGRS Research Map | TGRS