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THz-TSI: A Terahertz Wireless Through-Silicon Interconnect Module for Advanced 3-D Integration Applications

THz-TSI: A Terahertz Wireless Through-Silicon Interconnect Module for Advanced 3-D Integration Applications 150 150
Author(s): Chen Jiang, Xiaodi Feng, Xiaohan Shen, Chixiao Chen, Qi Liu, Ming Liu, Ningsheng Xu

Abstract:

By vertically stacking device layers, advanced 3-D integration overcomes traditional planar scaling limits, delivering the bandwidth and power efficiency essential for modern artificial intelligence (AI) and high-performance computing (HPC) integrated systems. However, continued scaling of conventional through-silicon vias (TSVs) and microbumps presents critical bottlenecks due to inherent thermo-mechanical stress, complex …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3720291
Publisher: IEEE

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HyFPCiM: A 65-nm 417-μW Error-Sensitivity-Aware FP8 Compute-in-Memory Macro

HyFPCiM: A 65-nm 417-μW Error-Sensitivity-Aware FP8 Compute-in-Memory Macro 150 150
Author(s): Gaurav Kumar K, Yatharth Agarwal, Kaushik Roy

Abstract:

This letter presents HyFPCiM, a 65-nm FP8 compute-in-memory (CiM) macro that enables sub-mW floating-point (FP) inference using error-sensitivity-aware FP partitioning (EAP). EAP maps exponent processing to a digital CiM (DCiM) path and mantissa accumulation to an analog CiM (ACiM), avoiding the power- and area-intensive adder-tree-based accumulation used in prior FP-CiM …

Published in: IEEE Solid-State Circuits Letters
Page(s): 146 – 149
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3688988
Publisher: IEEE

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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

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