Digital circuits

  • No Comments

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

View on IEEE Xplore

  • No Comments

A 28-nm System-in-One-Macro Computing-in-Memory Chip Utilizing Leakage-Eliminated 2T1C and Capacitor-Over-Logic 1T1C eDRAM

A 28-nm System-in-One-Macro Computing-in-Memory Chip Utilizing Leakage-Eliminated 2T1C and Capacitor-Over-Logic 1T1C eDRAM 150 150
Author(s): Zhihang Qian, Zhaori Cong, Shengzhe Yan, Zeyu Guo, Zhuoyu Dai, Yifan He, Wenyu Sun, Chunmeng Dou, Feng Zhang, Di Geng, Jinshan Yue, Yongpan Liu, Qi Liu, Ming Liu

Abstract:

Computing-in-memory (CIM) is a promising paradigm for energy- and area-efficient implementation of the heavy general matrix multiplication (GEMM) operations, especially in the evolving deep learning algorithms. Though existing CIM macros have demonstrated remarkable energy/area efficiency, the corresponding metrics of the system-level CIM chips degrade due to the peripheral components, …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 15
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3684971
Publisher: IEEE

View on IEEE Xplore

Search this website