Energy efficiency

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FSL-HDnn: A 40-nm Few-Shot On-Device Learning Accelerator With Integrated Feature Extraction and Hyperdimensional Computing

FSL-HDnn: A 40-nm Few-Shot On-Device Learning Accelerator With Integrated Feature Extraction and Hyperdimensional Computing 150 150
Author(s): Weihong Xu, Chang Eun Song, Haichao Yang, Leo Liu, Meng-Fan Chang, Carlos H. Diaz, Tajana Rosing, Mingu Kang

Abstract:

This article introduces FSL-HDnn, an energy-efficient accelerator that implements the end-to-end pipeline of feature extraction (FE) and on-device few-shot learning (FSL). The accelerator addresses fundamental challenges of on-device learning (ODL) for resource-constrained edge applications through two synergistic modules: a parameter-efficient feature extractor (FE) employing weight clustering and an FSL classifier …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3709217
Publisher: IEEE

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A 92.1-dB SNDR Easy-Driving Two-Step NS-SAR-Based Incremental ADC With Concurrent Gain-Error Plus Noise Suppression

A 92.1-dB SNDR Easy-Driving Two-Step NS-SAR-Based Incremental ADC With Concurrent Gain-Error Plus Noise Suppression 150 150
Author(s): Chenyang Li, Tzu-Han Wang, Dongsuk Kang, Ken Li, Xitie Zhang, Wei-En Lee, Visvesh S. Sathe, Shaolan Li

Abstract:

This article presents a two-step incremental analog-to-digital converter (ADC) that achieves high resolution and energy efficiency while substantially easing the input driving constraints and interstage gain variation. By employing a level-shifted sub-ranging architecture with an input-tracking (IT) feature, the design obviates direct input sampling, thereby significantly relaxing the demands on …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 884 – 897
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3635146
Publisher: IEEE

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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

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Modern Wireline Transceivers

Modern Wireline Transceivers 150 150
Author(s): Tony Chan Carusone, Timothy O. Dickson, Samuel Palermo, Sudip Shekhar, Mozhgan Mansuri

Abstract:

Over the past two decades, ever-increasing network bandwidth (BW) demands in data centers and high-performance computing systems have fueled exponential growth in per-lane serial link data rates. To keep up with this demand and enable faster communication over BW-limited electrical channels, wireline transceiver architectures and circuit topologies have rapidly evolved …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 395 – 422
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3642231
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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TexCAC: A Direct-Textile-Attachable Microcontroller Integrating 2-MB MRAM for the Command and Control of Advanced Smart Textiles

TexCAC: A Direct-Textile-Attachable Microcontroller Integrating 2-MB MRAM for the Command and Control of Advanced Smart Textiles 150 150
Author(s): Jacob Mack, Qirui Zhang, He Wang, Yi Shen, Jeongtaek Chang, Heejin Yang, Anjali Agrawal, Zichen Fan, Braden Desman, Zane Lewis, Thomas Farrell, David Blaauw, Will Farrell, Jim Owens, Benton H. Calhoun, Dennis Sylvester

Abstract:

Advanced smart textiles (ASTs) are textile-integrated electronic systems that enable many applications, including healthcare, robotics, and IoT. ASTs contain a variety of electronic components to enable whole system functionality (batteries, sensors, SoCs, etc.), which all require orchestration from a central command-and-control (CAC) module. The primary functions of the CAC module …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 10
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3695482
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 BEV Perception Transformer Accelerator With Saliency-Driven Image/Point Cloud Fusion and Phase-Linked Dataflow in 28 nm CMOS

A BEV Perception Transformer Accelerator With Saliency-Driven Image/Point Cloud Fusion and Phase-Linked Dataflow in 28 nm CMOS 150 150
Author(s): Junyi Qian, Yuncheng Lu, Zhangrui Qian, Lishuo Deng, Zhihao Yan, Cai Li, Tony Tae-Hyoung Kim, Weiwei Shan

Abstract:

Deploying advanced Transformer-based models for real-time, high-accuracy multimodal bird’s-eye-view (BEV) perception in autonomous driving imposes substantial hardware demands. To address this, we propose a low-cost, low-power image/point-cloud fusion Transformer accelerator that supports two modes: high-performance driving and ultra-low-power sentry operation. We first propose a cross-modal saliency evaluation mechanism …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3691770
Publisher: IEEE

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