Computer architecture

  • No Comments

Open DRAM Model—Part II: Enabling Processing-in-Memory in 3-D DRAM

Open DRAM Model—Part II: Enabling Processing-in-Memory in 3-D DRAM 150 150
Author(s): Kiseok Lee, Seongkwang Lim, Suman Datta, Shimeng Yu

Abstract:

Processing-in-memory (PIM) by implementing Boolean logic functions in dynamic random access memory (DRAM) has been proposed to alleviate the memory wall problem in data-intensive computing. However, quantitatively evaluating DRAM-based logic operations across different DRAM architectures remains challenging due to the lack of publicly available DRAM cell and peripheral transistor models …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 144 – 152
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3704508
Publisher: IEEE

View on IEEE Xplore

  • No Comments

A Low-Power MRAM-Based Nonvolatile Flip-Flop Architecture for Register and System Applications

A Low-Power MRAM-Based Nonvolatile Flip-Flop Architecture for Register and System Applications 150 150
Author(s): Bayartulga Ishdorj, Taehui Na

Abstract:

This work presents a minimal nonvolatile flip-flop (NVFF) hybrid architecture and an NV static contention-free differential FF (nvSCDFF) cell that reduces the overhead associated with replicated NV circuitry in conventional in situ NVFFs while enabling low power. By using only one NVFF per column and compact SCDFF-based volatile flip-flops (FFs), …

Published in: IEEE Solid-State Circuits Letters
Page(s): 206 – 209
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3713204
Publisher: IEEE

View on IEEE Xplore

  • No Comments

A Continuous-Time Incremental Zoom ADC With Extended Quantization, Easy-to-Drive Capacitive Input and Deadband-Embedded Gm-C Loop Filter

A Continuous-Time Incremental Zoom ADC With Extended Quantization, Easy-to-Drive Capacitive Input and Deadband-Embedded Gm-C Loop Filter 150 150
Author(s): Chaoyang Xing, Yilu Cui, Yi Zhong, Nan Sun, Lu Jie

Abstract:

This article presents a continuous-time (CT) analog-to-digital converter (ADC) featuring an incremental zoom with extended quantization architecture. The proposed MASH topology comprises an 8-bit oversampled successive-approximation register (SAR) ADC as the first stage, a 1-bit incremental CT delta-sigma modulator (CT-DSM) as the second stage, and a 12-bit Nyquist SAR ADC …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3717666
Publisher: IEEE

View on IEEE Xplore

  • No Comments

A 3-nm FinFET 563-kbit 35.5-Mbit/mm2 Dual-Rail SRAM With 3.89-pJ/Access High Energy Efficient and 27.5-μW/Mbit One-Cycle Latency Low-Leakage Mode

A 3-nm FinFET 563-kbit 35.5-Mbit/mm2 Dual-Rail SRAM With 3.89-pJ/Access High Energy Efficient and 27.5-μW/Mbit One-Cycle Latency Low-Leakage Mode 150 150
Author(s): Yumito Aoyagi, Koji Nii, Makoto Yabuuchi, Tomotaka Tanaka, Kazuto Mizutani, Masaya Hamada, Yuichiro Ishii, Yen Chi Chou, Jun Cheng Liu, Chien-Yu Huang, Kang-Hui Peng, Yu-Hao Hsu, Isabel Wang, Hong-Chen Cheng, Hung-Jen Liao, Tsung-Yung Jonathan Chang

Abstract:

This article presents a high-density (HD) 6T SRAM macro designed in 3-nm FinFET technology with an extended dual-rail (XDR) architecture, addressing active energy and leakage for mobile applications. Two key innovations are introduced: the delayed-wordline in write operation (DEWL) technique and a one-cycle latency low-leakage access mode (1-CLM). The XDR …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1466 – 1476
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3639747
Publisher: IEEE

View on IEEE Xplore

  • No Comments

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

View on IEEE Xplore

  • No Comments

A 108-Gb/s PAM-8 CTLE + FFE Receiver Front-End With 1.4-Vppd Input Range in 28-nm CMOS

A 108-Gb/s PAM-8 CTLE + FFE Receiver Front-End With 1.4-Vppd Input Range in 28-nm CMOS 150 150
Author(s): Sangwan Lee, Heedo Jeong, Hanhee Jo, Hwanseok Jung, Jaeduk Han

Abstract:

This article presents a 108-Gb/s 8-level pulse amplitude modulation (PAM-8) receiver front-end capable of handling a wide input dynamic range to overcome the signal-to-noise ratio (SNR) versus linearity tradeoff in higher-order modulation schemes. The proposed architecture employs a multi-path continuous-time linear equalizer (CTLE) with a bias-shifting technique, which splits …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3713392
Publisher: IEEE

View on IEEE Xplore

  • No Comments

A 32-Channel 85.4 dB SNDR Time-Multiplexed Neural Recording Front-End Achieving Within-Conversion Artifact Recovery

A 32-Channel 85.4 dB SNDR Time-Multiplexed Neural Recording Front-End Achieving Within-Conversion Artifact Recovery 150 150
Author(s): Arindam Mandal, Chi-Hsiang Huang, Julian Arenas, Wei-En Lee, Kevin D. Patino-Sosa, Philip Anschutz, Amanda Jacob, Keshav Ramachandra, Samuel Sober, Muhannad S. Bakir, Shaolan Li, Visvesh S. Sathe

Abstract:

This article presents a 32-channel time-multiplexed highly digital neural recording front-end (RFE) that exhibits sub- $8.8~\mu $ s recovery latency from large stimulation artifacts while delivering high-resolution data at the Nyquist rate. The RFE is time-shared across 32 channels for low-frequency electrocorticography (ECoG) recording and across four channels for high-frequency action potentials (…

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1053 – 1067
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3643367
Publisher: IEEE

View on IEEE Xplore

  • No Comments

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

View on IEEE Xplore

  • No Comments

A 11.0-TOPS/W Diffusion Accelerator With Temporal Data Reuse for Real-Time Text-to-Motion Generation

A 11.0-TOPS/W Diffusion Accelerator With Temporal Data Reuse for Real-Time Text-to-Motion Generation 150 150
Author(s): Sungwoong Yune, Jieon Yoon, Hangyeol Lee, Adiwena Putra, Jaehoon Heo, Joo-Young Kim

Abstract:

Text-to-motion models are AI systems that generate human motion sequences directly from natural language descriptions, serving as key enablers for immersive virtual avatars and interactive digital humans in AR/VR ecosystems. However, state-of-the-art text-to-motion diffusion models suffer from substantial computational costs due to their iterative nature, making them ill-suited for …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3689546
Publisher: IEEE

View on IEEE Xplore

Search this website