Common Information Model (electricity)

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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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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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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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