Correlation

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MixCIM: A Hybrid Computing-in-Memory Macro With Less Data-Movement and Better Memory-Reuse for Depthwise Separable Neural Networks

MixCIM: A Hybrid Computing-in-Memory Macro With Less Data-Movement and Better Memory-Reuse for Depthwise Separable Neural Networks 150 150
Author(s): Xin Qiao, Youming Yang, Jiahao Song, Renjie Wei, Xiyuan Tang, Meng Li, Runsheng Wang, Yuan Wang

Abstract:

Computing-in-memory (CIM) architectures have demonstrated strong potential for edge artificial intelligence (AI) devices due to their enhanced parallelism and energy efficiency. With the growing complexity of AI tasks and the rapid increase in model size, computation and deployment costs have surged. Depthwise separable neural networks (DSNNs) have attracted interest for …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3651775
Publisher: IEEE

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Fully Analog, Multi-Lag, RF Correlators for Code-Domain Radars Using Margin Propagation

Fully Analog, Multi-Lag, RF Correlators for Code-Domain Radars Using Margin Propagation 150 150
Author(s): Aswin Undavalli, Kareem Rashed, Abdul Rakib Hossain, Gert Cauwenberghs, Shantanu Chakrabartty, Arun Natarajan, Aravind Nagulu

Abstract:

We present a fully analog, multiplier-free, sampled-domain RF correlator to achieve high energy efficiency for radar workloads. The RF correlator employs a split-source follower architecture that leverages the margin propagation (MP) computing paradigm in the sampled domain. As a proof of concept, we implement a $256 \times 256$ fully analog cross correlator …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 16
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3602920
Publisher: IEEE

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