multiply-and-accumulate (MAC)

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A Multiply-and-Accumulate SAR-ADC-Based Hybrid Slepian Beamformer

A Multiply-and-Accumulate SAR-ADC-Based Hybrid Slepian Beamformer 150 150
Author(s): Zhengqi Xu, Zhiyuan Zhao, Michael A. Laun, Coleman DeLude, Justin Romberg, Michael P. Flynn

Abstract:

This article introduces a hybrid Slepian beamforming receiver architecture with low power and area costs. Traditional large-scale true-time-delay (TTD) beamformers for wideband wireless communication suffer from high power consumption and high hardware costs. As an alternative, the Slepian beamforming approach reduces the number of analog-to-digital conversions (ADCs) and delays for …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3651452
Publisher: IEEE

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A Microscaling Multi-Mode Gain-Cell Computing-in-Memory Macro for Advanced AI Edge Device

A Microscaling Multi-Mode Gain-Cell Computing-in-Memory Macro for Advanced AI Edge Device 150 150
Author(s): Jen-Chun Tien, Ping-Chun Wu, Win-San Khwa, Ashwin Sanjay Lele, Jian-Wei Su, Chiao-Yen Cheng, Jun-Ming Hsu, Yu-Chen Chen, Le-Jung Hsieh, Jyun-Cheng Bai, Yu-Sheng Kao, Tsung-Han Lou, Jui-Jen Wu, Chung-Chuan Lo, Ren-Shuo Liu, Chih-Cheng Hsieh, Kea-Tiong Tang, Meng-Fan Chang

Abstract:

The microscaling (MX) format is an emerging data representation that quantizes high-bitwidth floating-point (FP) values into low-bitwidth FP-like values with a shared-scale (SS) exponent. When implemented with computing-in-memory (CIM), MX allows an attractive tradeoff between accuracy and hardware efficiency for specific neural network (NN) workloads. This work presents the first …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3617944
Publisher: IEEE

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