Very large scale integration

  • No Comments

A 28-nm Digital Transpose SRAM Compute-in-Memory Macro With Accurate/Approximate Dual Mode for Floating-Point Edge Training and Inference

A 28-nm Digital Transpose SRAM Compute-in-Memory Macro With Accurate/Approximate Dual Mode for Floating-Point Edge Training and Inference 150 150
Author(s): Yiyang Yuan, Bingxin Zhang, Yiming Yang, Yishan Luo, Qirui Chen, Haitao Wang, Qihao Liu, Zhiming Chen, Hao Wu, Jinshan Yue, Shidong Lv, Xinghua Wang, Pui-In Mak, Xiaoran Li, Feng Zhang

Abstract:

Static random-access memory (SRAM)-based computing-in-memory (CIM) macros have been widely studied to improve the energy efficiency of edge artificial intelligence (AI) inference tasks. However, less attention has been given to AI training, which requires CIM macros to not only perform matrix multiply-accumulate (MAC) operations but also support matrix transposition. …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3679560
Publisher: IEEE

View on IEEE Xplore

  • No Comments

A Wide-Dynamic-Range Photovoltaic Energy Harvester With Adaptive Power-Scalable MPPT Control and Direct Power-to-Digital Converter

A Wide-Dynamic-Range Photovoltaic Energy Harvester With Adaptive Power-Scalable MPPT Control and Direct Power-to-Digital Converter 150 150
Author(s): Yuyan Liu, Huajun Zhang, Mustafa Becermis, Jieyu Liao, Mohit Dandekar, Kofi A. A. Makinwa, Qinwen Fan

Abstract:

This article presents a photovoltaic energy harvester (PVEH) that achieves high maximum power point tracking (MPPT) efficiency and power conversion efficiency across a $100~000{\times }$ input power dynamic range (DR) (from $10~{\mu }$ W to 1W). Wide-dynamic-range operation is challenging due to the inherent tradeoff between MPPT accuracy and controller power consumption. …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3675521
Publisher: IEEE

View on IEEE Xplore

  • No Comments

PERCEL: A Rewritable NVM CIM Incorporating a CTT-Based Per-Cell DAC

PERCEL: A Rewritable NVM CIM Incorporating a CTT-Based Per-Cell DAC 150 150
Author(s): Samyak Chakrabarty, Vinod Jacob, Mohammadreza Zeinali, Georgios Karfakis, Siyun Qiao, Ziyi Guo, Puneet Gupta, Subramanian Iyer, Sudhakar Pamarti

Abstract:

Compute-in-memory (CiM) accelerators perform matrix vector multiplications (MVMs) directly inside memory arrays, reducing data movement and improving both energy efficiency and throughput for artificial intelligence (AI) workloads. To reduce the number of conversions, recent designs use multibit compute cells. Nevertheless, practical multibit CiM still faces a tension among accuracy, efficiency, …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 70 – 77
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3680497
Publisher: IEEE

View on IEEE Xplore

Search this website