In-memory computing

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A 194.6-TOPS/W Pipelined All Current-Domain Mixed-Signal Compute in Memory in 28-nm CMOS

A 194.6-TOPS/W Pipelined All Current-Domain Mixed-Signal Compute in Memory in 28-nm CMOS 150 150
Author(s): Saideep Cherukuri, N. V. Kidambi, W. C. Wang, I. Chakraborty, S. Zhang, L. Shamieh, J. B. Steinberg, S. Mukhopadhyay

Abstract:

Mixed-signal CIM (MS-CIM) faces bit-cell nonlinearity, poor linearity at high frequency, and throughput limits. We present a hybrid pipelined current-domain MS-CIM macro featuring bit-cell matched linearization interface (BMLI) and loop-unrolled successive approximation refinement (SAR) ADC fabricated in 28-nm CMOS. A $256{\,}\times {\,}256$ SRAM array with 8-bit inputs, 8-bit weights achieve 10.16-TOPS …

Published in: IEEE Solid-State Circuits Letters
Page(s): 174 – 177
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3697990
Publisher: IEEE

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Leveraging a Passive MRAM Crossbar for Hardware-in-the-Loop and Continual learning

Leveraging a Passive MRAM Crossbar for Hardware-in-the-Loop and Continual learning 150 150
Author(s): M. Drouhin, M. Grelier, M. Deroo, Y. Sassi, E. Monteblanco, N. Davila, G. Mihajlović, R. V. Chopdekar, T. S. Santos, J. A. Katine, D. Ravelosona

Abstract:

Artificial neural networks (ANNs) have enabled major advances in artificial intelligence, yet their growing computational and energy demands challenge conventional von Neumann architectures due to the costly separation of memory and processing. In-memory computing has emerged as a promising solution, particularly through memristive crossbar arrays capable of performing multiply-and-accumulate operations …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 129 – 134
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3702001
Publisher: IEEE

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A Nonvolatile AI-Edge Processor With Lossless-Compressed-Computing STT-MRAM Near-Memory-Compute Macro Using Dynamic Floating-/Fixed-Point Accumulation

A Nonvolatile AI-Edge Processor With Lossless-Compressed-Computing STT-MRAM Near-Memory-Compute Macro Using Dynamic Floating-/Fixed-Point Accumulation 150 150
Author(s): De-Qi You, Win-San Khwa, Bo Zhang, Jui-Jen Wu, Chuan-Jia Jhang, Guan-Yi Lin, Po-Jung Chen, Ting-Chien Chiu, Fang-Yi Chen, Andrew Lee, Yu-Cheng Hung, Chung-Chuan Lo, Ren-Shuo Liu, Chih-Cheng Hsieh, Kea-Tiong Tang, Yu-Der Chih, Tsung-Yung Jonathan Chang, Meng-Fan Chang

Abstract:

Nonvolatile AI-edge processors based on near-memory-compute (nvNMC) enable energy-efficient multiply-and-accumulate (MAC) operations with short wakeup latency for edge inference operations. Lossless compression is required for floating-point (FP) neural network (NN) models under on-chip memory capacity constraints; however, this imposes several challenges: 1) data decompression overhead due to lossless FP weight encoding; 2) …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3705432
Publisher: IEEE

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A 3 nm FinFET 125 TOPS/W-29 TFLOPS/W, 90 TOPS/mm2-17 TFLOPS/mm2 SRAM-Based INT8, and FP16 Digital-CIM Compiler With Support for Multi-Weight Update/Cycle

A 3 nm FinFET 125 TOPS/W-29 TFLOPS/W, 90 TOPS/mm2-17 TFLOPS/mm2 SRAM-Based INT8, and FP16 Digital-CIM Compiler With Support for Multi-Weight Update/Cycle 150 150
Author(s): Haruki Mori, Je-Min Hung, Wei-Chang Zhao, Kinshuk Khare, Brian Crafton, Hiroaki Ishii, Cheng-En Lee, Xiaochen Peng, Xiaoyu Sun, Yorinobu Fujino, Chia-Jung Tsen, Vineet Joshi, Chao-Kai Chuang, Takeshi Hashizume, Chia-Fu Lee, Tan-Li Chou, Kerem Akarvardar, Saman Adham, Yih Wang, Hidehiro Fujiwara, Yu-Der Chih, Yen-Huei Chen, Hung-Jen Liao, Tsung-Yung Jonathan Chang

Abstract:

This article presents an static random-access memory (SRAM)-based digital compute-in-memory (CIM) compiler implemented with 3 nm high- $\kappa $ metal gate (HKMG) FinFET technology, supporting flexible INT8 and FP16 formats for weight and activation multiply-accumulate (MAC) operations, offering configuration flexibility, high accuracy, and improved area and power efficiency. The FP16 digital …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1502 – 1512
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3646072
Publisher: IEEE

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STAR-SRAM: 16-bit Floating-Point SRAM-Based Digital Computing-in-Memory Macro in a 28 nm

STAR-SRAM: 16-bit Floating-Point SRAM-Based Digital Computing-in-Memory Macro in a 28 nm 150 150
Author(s): Chuan-Tung Lin, Jonghyun Oh, Mingoo Seok

Abstract:

A digital computing-in-memory (DCIM) macro emerges as a promising building block in a deep neural network (DNN) accelerator. To better support DNN workloads, circuit designers aim to improve three main metrics for macros: energy efficiency, compute density, and weight density. Improvements in those metrics directly translate into reduced energy consumption, …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3694927
Publisher: IEEE

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A 28-nm FD-SOI CMOS Analog-IMC Core Based on PCM Featuring 8 512 × 512-Weight Layers and 28M Weights×TOPs/W/mm2

A 28-nm FD-SOI CMOS Analog-IMC Core Based on PCM Featuring 8 512 × 512-Weight Layers and 28M Weights×TOPs/W/mm2 150 150
Author(s): Marco Pasotti, Riccardo Zurla, J. J. Bertolini Agnoletto, Emanuela Calvetti, Alessio Antolini, Andrea Lico, Giuseppe Desoli, Riccardo Vignali, Luca Iannelli, Luigi Croce, Francesco Zavalloni, Eleonora Franchi Scarselli, Alessandro Cabrini

Abstract:

In-memory computing (IMC) hardware accelerators for deep neural networks (DNNs) require storing a massive number of coefficients within a single computing macro to avoid performance degradation in multicore clusters. This aspect, often overlooked by common figures of merit (FoMs), can be effectively addressed by phase-change memory (PCM) technology, thanks to …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 15
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3693894
Publisher: IEEE

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A 28-nm PVT Inner-Tracking Time-Domain Compute-In-Memory Macro for Edge-AI Devices

A 28-nm PVT Inner-Tracking Time-Domain Compute-In-Memory Macro for Edge-AI Devices 150 150
Author(s): Yuanzhe Zhao, Yuheng Wang, Heng Xie, Zijian Wang, Rui P. Martins, Chi-Hang Chan, Minglei Zhang

Abstract:

This article presents an energy-efficient and process-, voltage-, and temperature (PVT)-robust time-domain (TD) compute-in-memory (CIM) macro for edge artificial intelligence (AI) devices. It features: 1) a PVT inner-tracking (PIT) technique that aligns the PVT responses of TD computation and TD quantization, delivering inherent robustness without incurring extra power or circuit …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3691189
Publisher: IEEE

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EMO-CIM: An Input/Stationary-Data Similarity-Aware Computing-In-Memory Design for Variable Vector-Wise Computation in Edge Multioperator AI Acceleration

EMO-CIM: An Input/Stationary-Data Similarity-Aware Computing-In-Memory Design for Variable Vector-Wise Computation in Edge Multioperator AI Acceleration 150 150
Author(s): Bo Wang, Yuchen Tang, Feiran Liu, Xiaoxue Zhong, Yuchen Ma, Xin Si, Jun Yang

Abstract:

We propose an edge multioperator computing-in-memory (EMO-CIM) design that supports variable vector-wise multiply-and-accumulate (MAC) in CNN, Depthwise (DW)-Convolution, and Attention operators. It features: 1) a single EMO-CIM bank (ECB) excels in variable vector-wise MAC (V-MAC) for multioperators; 2) merging local input-shared compute units (LISCUs) with a decode-unit and adder-tree (DUAT) facilitates …

Published in: IEEE Solid-State Circuits Letters
Page(s): 97 – 100
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3668853
Publisher: IEEE

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A Folded-Differential Switched-Capacitor SRAM CIM Macro With Scalable MAC Sizes for TinyML Inference

A Folded-Differential Switched-Capacitor SRAM CIM Macro With Scalable MAC Sizes for TinyML Inference 150 150
Author(s): Zhonghao Chen, Ling-An Cheong, Tianyi Yu, Yiming Chen, Guodong Yin, Teng Yi, Yongpan Liu, Huazhong Yang, Xueqing Li

Abstract:

This letter presents a switched-capacitor SRAM compute-in-memory macro optimized for TinyML inference. Key features include: 1) an area-efficient folded-differential multiply-and-accumulate (FD-MAC) scheme to double the signal margin; 2) a closed-loop floating-inverter amplifier (FIA)-based charge accumulation technique for signal-to-noise ratio enhancement and multiply-and-accumulate (MAC) voltage integration; and 3) a sparsity-aware multistep MAC method …

Published in: IEEE Solid-State Circuits Letters
Page(s): 73 – 76
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3662477
Publisher: IEEE

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