Re-Meltrix: A Reconfigurable Processing-in-Memory Architecture Based on RRAM and Function Synthesis

The reconfigurable processing-in-memory (PIM) architecture has garnered significant attention in recent years due to its versatility and ability to overcome storage limitations. However, it faces challenges, such as overly complex mapping and routing caused by the fine granularity of basic logic units, and the inclusion of numerous redundant devices to achieve reconfigurability. To address these issues, we have designed a software-hardware co-design reconfigurable PIM architecture called Re-Meltrix. Its hardware architecture uses an resistive random-access memory array as the foundation, combined with well-designed peripheral circuits. Maintaining a controllable area, it integrates logic, storage, ternary content-address memory, and interconnection modes into a unified tile architecture and implements two-level independent interconnection within and between tiles. This approach achieves a single tile logic capacity multiple times that of the most advanced reconfigurable PIM architectures currently available, thereby resolving mapping and routing difficulties at the hardware level. Our proposed function synthesis, combined with the hardware architecture, specifically optimizes two-level interconnection separation and module segmentation, further reducing interconnection complexity and improving tile usage efficiency. Experiments have demonstrated that our architecture outperforms the state-of-the-art Liquid Silicon by 2.00–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.31\times $ </tex-math></inline-formula> in performance and reduces power consumption by 29%–68%. Compared with the previously published Meltrix, the area has decreased by 15%–35%, with the area and power consumption remaining almost unchanged.

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Publication Details

Journal
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Published
2025-02-12
DOI
https://doi.org/10.1109/tcad.2025.3541487
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Re-Meltrix: A Reconfigurable Processing-in-Memory Architecture Based on RRAM and Function Synthesis

Yinhe Han, Xian‐He Sun, Boyu Long, Xiaoming Chen
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Advanced Memory and Neural Computing
article

Re-Meltrix: A Reconfigurable Processing-in-Memory Architecture Based on RRAM and Function Synthesis

Yinhe Han, Xian‐He Sun, Boyu Long, Xiaoming Chen
article en

Abstract

The reconfigurable processing-in-memory (PIM) architecture has garnered significant attention in recent years due to its versatility and ability to overcome storage limitations. However, it faces challenges, such as overly complex mapping and routing caused by the fine granularity of basic logic units, and the inclusion of numerous redundant devices to achieve reconfigurability. To address these issues, we have designed a software-hardware co-design reconfigurable PIM architecture called Re-Meltrix. Its hardware architecture uses an resistive random-access memory array as the foundation, combined with well-designed peripheral circuits. Maintaining a controllable area, it integrates logic, storage, ternary content-address memory, and interconnection modes into a unified tile architecture and implements two-level independent interconnection within and between tiles. This approach achieves a single tile logic capacity multiple times that of the most advanced reconfigurable PIM architectures currently available, thereby resolving mapping and routing difficulties at the hardware level. Our proposed function synthesis, combined with the hardware architecture, specifically optimizes two-level interconnection separation and module segmentation, further reducing interconnection complexity and improving tile usage efficiency. Experiments have demonstrated that our architecture outperforms the state-of-the-art Liquid Silicon by 2.00–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.31\times $ </tex-math></inline-formula> in performance and reduces power consumption by 29%–68%. Compared with the previously published Meltrix, the area has decreased by 15%–35%, with the area and power consumption remaining almost unchanged.

IEEE Transactions on Computer-Aided Design of Integrated Circuits and SystemsVol. 44(9)
Illinois Institute of Technology (US), Chinese Academy of Sciences (CN), Institute of Computing Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 98%
Advanced Memory and Neural Computing
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