Cross-Layer Design and Design Automation for In-Memory Computing Based on Nonvolatile Memory Technologies

Data transfer between processors and memory remains a critical bottleneck in improving application performance on traditional computing hardware, particularly for data-intensive workloads such as machine learning, bioinformatics, and security applications. In-memory computing (IMC), a paradigm where a substantial portion of data processing occurs directly within memory, has emerged as a promising solution to mitigate this bottleneck. The advancement of emerging non-volatile memory (NVM) technologies has further accelerated the development of IMC hardware fabrics. However, harnessing the full potential of IMC requires a cross-layer design approach that spans memory technologies, circuits, architectures, and systems. Essential cross-layer tools—including modeling and simulation, data partitioning and mapping, and operation scheduling—play a pivotal role in designing efficient IMC-based hardware. This article reviews key advancements in simulation and design tools for IMC fabrics, with a focus on NVM-based crossbar arrays and content-addressable memories, while highlighting the necessity of cross-layer collaboration. Additionally, we discuss current challenges and emerging opportunities in the field.

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Publication Details

Journal
IEEE Design and Test
Published
2025-08-28
DOI
https://doi.org/10.1109/mdat.2025.3603495
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

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article

Cross-Layer Design and Design Automation for In-Memory Computing Based on Nonvolatile Memory Technologies

Michael Niemier, João Paulo C. de Lima, Jerónimo Castrillón, Yu Wang et al.
1 citations
IEEE Design and Test
Advanced Memory and Neural Computing
0.73
article

Cross-Layer Design and Design Automation for In-Memory Computing Based on Nonvolatile Memory Technologies

Michael Niemier, João Paulo C. de Lima, Jerónimo Castrillón, Yu Wang, Liu Liu, Xiaobo Sharon Hu, Minah Lee, Zhenhua Zhu, Mengyuan Li
article en
1 citations

Abstract

Data transfer between processors and memory remains a critical bottleneck in improving application performance on traditional computing hardware, particularly for data-intensive workloads such as machine learning, bioinformatics, and security applications. In-memory computing (IMC), a paradigm where a substantial portion of data processing occurs directly within memory, has emerged as a promising solution to mitigate this bottleneck. The advancement of emerging non-volatile memory (NVM) technologies has further accelerated the development of IMC hardware fabrics. However, harnessing the full potential of IMC requires a cross-layer design approach that spans memory technologies, circuits, architectures, and systems. Essential cross-layer tools—including modeling and simulation, data partitioning and mapping, and operation scheduling—play a pivotal role in designing efficient IMC-based hardware. This article reviews key advancements in simulation and design tools for IMC fabrics, with a focus on NVM-based crossbar arrays and content-addressable memories, while highlighting the necessity of cross-layer collaboration. Additionally, we discuss current challenges and emerging opportunities in the field.

IEEE Design and TestVol. 42(6)
University of Notre Dame (US), Georgia Institute of Technology (US), Technische Universität Dresden (DE), Tsinghua University (CN)
Semiconductor Research Corporation
Openalex Percentile: Top 24%
Advanced Memory and Neural Computing
0.73
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