In-memory computing

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A Multicore Programmable Variable-Precision Near-Memory Accelerator for CNN and Transformer Models

A Multicore Programmable Variable-Precision Near-Memory Accelerator for CNN and Transformer Models 150 150
Author(s): Yiming Yang, Yiyang Yuan, Xinghua Wang, Xiaoran Li, Hao Wu, Qihao Liu, Weiye Tang, Xiangqu Fu, Feng Zhang

Abstract:

Convolutional neural network (CNN) and transformer are the most popular neural network models in computer vision (CV) and natural language processing (NLP). It is quite common to use both these two models in multimodal scenarios, such as text-to-image generation. However, these two models have very different memory mappings, dataflows and …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3624011
Publisher: IEEE

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Energy-efficient Logic-in-memory and Neuromorphic Computing in RSD MOSFETs

Energy-efficient Logic-in-memory and Neuromorphic Computing in RSD MOSFETs 150 150
Author(s): Md. Hasan Raza Ansari, Bashayr Alqahtani, Naveen Kumar, Vihar Georgiev, Nazek El-Atab

Abstract:

This work highlights the potential application of raised source and drain (RSD) MOSFETs-based charge trapping memory (CTM) for next-generation computing applications. This simulation study presents a double gate (DG)-RSD MOSFET technology with a short gate length (50 nm) to significantly improve the performance of logic-in-memory (LIM) and neuromorphic computing (NC) …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 1 – 1
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3630217
Publisher: IEEE

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DPe-CIM: A 4T-1C Dual-Port eDRAM-Based Compute-in-Memory for Simultaneous Computing and Refresh With Adaptive Refresh and Data Conversion Reduction Scheme

DPe-CIM: A 4T-1C Dual-Port eDRAM-Based Compute-in-Memory for Simultaneous Computing and Refresh With Adaptive Refresh and Data Conversion Reduction Scheme 150 150
Author(s): Do Han Kim, Minyoung Jo, Gi Seok Kim, Dong Gyun Ha, Ung Bin Oh, Donghan Ko, Jiwoo Lee, Mingu Kang, Seong-Ook Jung

Abstract:

This article presents DPe-CIM, a 4T-1C dual-port embedded dynamic random access memory (eDRAM)-based compute-in-memory (CIM) macro with adaptive refresh and data conversion reduction. DPe-CIM proposes four key features that improve area and energy efficiency: 1) dual-port eDRAM cell (DPC) separates the multiply-and-accumulate (MAC) and refresh ports, enabling simultaneous MAC …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3623117
Publisher: IEEE

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AACIM: A 2785-TOPS/W, 161-TOP/mm2, <1.17%-RMSE, Analog-In Analog-Out Computing-In-Memory Macro in 28 nm

AACIM: A 2785-TOPS/W, 161-TOP/mm2, <1.17%-RMSE, Analog-In Analog-Out Computing-In-Memory Macro in 28 nm 150 150
Author(s): Rentao Wan, Chuan-Tung Lin, Bo Zhang, Yannis Tsividis, Mingoo Seok

Abstract:

This article presents an analog-in analog-out CIM macro (AACIM) for use in analog deep neural network (DNN) processors. Our macro receives analog inputs, performs a 64-by-32 vector–matrix multiplication (VMM) with a current-discharging computation mechanism, and produces analog outputs. It stores a 4-bit weight as an analog voltage in the …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 11
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3622309
Publisher: IEEE

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A Microscaling Multi-Mode Gain-Cell Computing-in-Memory Macro for Advanced AI Edge Device

A Microscaling Multi-Mode Gain-Cell Computing-in-Memory Macro for Advanced AI Edge Device 150 150
Author(s): Jen-Chun Tien, Ping-Chun Wu, Win-San Khwa, Ashwin Sanjay Lele, Jian-Wei Su, Chiao-Yen Cheng, Jun-Ming Hsu, Yu-Chen Chen, Le-Jung Hsieh, Jyun-Cheng Bai, Yu-Sheng Kao, Tsung-Han Lou, Jui-Jen Wu, Chung-Chuan Lo, Ren-Shuo Liu, Chih-Cheng Hsieh, Kea-Tiong Tang, Meng-Fan Chang

Abstract:

The microscaling (MX) format is an emerging data representation that quantizes high-bitwidth floating-point (FP) values into low-bitwidth FP-like values with a shared-scale (SS) exponent. When implemented with computing-in-memory (CIM), MX allows an attractive tradeoff between accuracy and hardware efficiency for specific neural network (NN) workloads. This work presents the first …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3617944
Publisher: IEEE

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A 28-nm Computing-in-Memory Processor With Zig-Zag Backbone-Systolic CIM and Block-/Self-Gating CAM for NN/Recommendation Applications

A 28-nm Computing-in-Memory Processor With Zig-Zag Backbone-Systolic CIM and Block-/Self-Gating CAM for NN/Recommendation Applications 150 150
Author(s): Shengzhe Yan, Zhuoyu Dai, Zhaori Cong, Zeyu Guo, Yifan He, Wenyu Sun, Chunmeng Dou, Feng Zhang, Jinshan Yue, Yongpan Liu, Ming Liu

Abstract:

Computing-in-memory (CIM) chips have demonstrated promising energy efficiency for artificial intelligence (AI) applications such as neural networks (NNs), Transformer, and recommendation system (RecSys). However, several challenges still exist. First, a large gap between the macro and system-level CIM energy efficiency is observed. Second, several memory-dominate operations, such as embedding in …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 15
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3610539
Publisher: IEEE

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FIMA: A Scalable Ferroelectric Compute-in-Memory Annealer for Accelerating Boolean Satisfiability

FIMA: A Scalable Ferroelectric Compute-in-Memory Annealer for Accelerating Boolean Satisfiability 150 150
Author(s): Mohammad Khairul Bashar, T. H. Pantha, Z. Li, M. Farasat, S. Datta, V. Narayanan, S. Dutta, N. Shukla

Abstract:

In-memory compute kernels present a promising approach for addressing data-centric workloads. However, their scalability—particularly for computationally intensive tasks solving combinatorial optimization problems such as Boolean satisfiability (SAT), which are inherently difficult to decompose—remains a significant challenge. In this work, we propose a ferroelectric nonvolatile memory (NVM)-based compute-in-memory …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 81 – 89
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3603942
Publisher: IEEE

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MIX-ACIM: A 28-nm Mixed-Precision Analog Compute-in-Memory With Digital Feature Restoration for Vector-Matrix Multiplication

MIX-ACIM: A 28-nm Mixed-Precision Analog Compute-in-Memory With Digital Feature Restoration for Vector-Matrix Multiplication 150 150
Author(s): Wei-Chun Wang, Shida Zhang, Laith Shamieh, Narasimha Vasishta Kidambi, Isha Chakraborty, Saibal Mukhopadhyay

Abstract:

A mixed-precision analog compute-in-memory (Mix-ACIM) is presented for mixed-precision vector-matrix multiplication (VMM). The design features an all-analog current-domain fixed-point (FxP) VMM with floating-point conversion and feature restoration. A 28 nm CMOS test chip shows 41 TOPS/W and 24 TOPS/mm2 for FxP (8-bit input/weight and 12-bit output) and 24.18 TFLOPS/W and 3.3 …

Published in: IEEE Solid-State Circuits Letters
Page(s): 213 – 216
Year of Publication: 2025
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2025.3590757
Publisher: IEEE

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Device Nonideality-Aware Compute-in-Memory Array Architecting: Direct Voltage Sensing, I–V Symmetric Bitcell, and Padding Array

Device Nonideality-Aware Compute-in-Memory Array Architecting: Direct Voltage Sensing, I–V Symmetric Bitcell, and Padding Array 150 150
Author(s): Jianzi Jin, Shifan Gao, Cimang Lu, Xiang Qiu, Yi Zhao

Abstract:

A voltage sensing compute-in-memory (CIM) architecture has been designed to improve the analog computing accuracy, and a chip on 90-nm flash platform has been successfully fabricated, with the bidirectional operation enabled by the symmetric bitcell structure. By padding the weight sum to a global value for all bit lines (BLs), …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 19 – 24
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3539470
Publisher: IEEE

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