Media Access Control

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An Eight-Channel Direct Analog Sensor Fusion Core for On-the-Spot Time-Series Analysis in Edge AI

An Eight-Channel Direct Analog Sensor Fusion Core for On-the-Spot Time-Series Analysis in Edge AI 150 150
Author(s): Jia-le Cui, Yasuko Matsubara, Yasushi Sakurai, Noriyuki Miura

Abstract:

A time-series analysis on multi-channel sensor signals is critical for wide-range Artificial Intelligence (AI) applications, such as in a field of mobility, robot, and infrastructure monitoring. Edge-device data collection followed by cloud-based analysis suffers from high latency and power consumption. This article presents an edge-AI-oriented sensor fusion architecture, performing analog …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 11
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3726487
Publisher: IEEE

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APM-CIM: An Array Partition Multi-Macro CIM System With Dynamic Sparse Approximation for Neural Network Edge Applications

APM-CIM: An Array Partition Multi-Macro CIM System With Dynamic Sparse Approximation for Neural Network Edge Applications 150 150
Author(s): Bojun Zhang, Jinkai Wang, Zekun Wang, Xianan Zhu, Zhengkun Gu, Ziyuan Guo, Zhizhong Zhang, Kun Zhang, Weisheng Zhao, Yue Zhang

Abstract:

Computing-in-memory (CIM) has been widely investigated as a solution to the von Neumann bottleneck to reduce data transfer between memory and processor. However, existing CIM macros for neural network (NN) edge applications still suffer from high latency and energy consumption due to the correlation between data precision and the number …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 16
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3720241
Publisher: IEEE

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A 28-nm Sign-Bit-Embedded Bit-Parallel SRAM Compute-in-Memory Macro With Stage-Wise-Enabled Transition-Counting-Lines and Accumulators for Edge-AI Devices

A 28-nm Sign-Bit-Embedded Bit-Parallel SRAM Compute-in-Memory Macro With Stage-Wise-Enabled Transition-Counting-Lines and Accumulators for Edge-AI Devices 150 150
Author(s): Yunlong Liu, Lichen Feng, Lin Wu, Dong Li, Zhangming Zhu

Abstract:

Computing-in-memory (CIM) architectures are promising for edge-AI devices, as they mitigate the von Neumann bottleneck and reduce data movement. Bit-parallel CIM macros with direct memory mapping and low toggle rate are well-suited for computing systems. However, severe routing congestion, inefficient signed computation, and unnecessary toggling of intermediate signals in local …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3718075
Publisher: IEEE

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TD-dAJC: A 100 MS/s 2 pJ/Pixel Time-Domain Weight and Integrating-MAC-Based Direct Analog to MJPEG Compression for Video Sensor Nodes

TD-dAJC: A 100 MS/s 2 pJ/Pixel Time-Domain Weight and Integrating-MAC-Based Direct Analog to MJPEG Compression for Video Sensor Nodes 150 150
Author(s): Harshit Naman, Gourab Barik, Yudhajit Ray, Shreyas Sen

Abstract:

Always-on, high-quality video sensors are increasingly required in wearables, surveillance, and Internet-of-Things (IoT) devices, where stringent energy constraints make efficient in-sensor compression necessary. These systems must support high-definition (HD) and 4 K video, yet conventional digital video compression pipelines consume significant power because high-speed analog-to-digital converters (ADCs) must digitize every pixel, …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 16
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3712196
Publisher: IEEE

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A Nonvolatile AI-Edge Processor With Lossless-Compressed-Computing STT-MRAM Near-Memory-Compute Macro Using Dynamic Floating-/Fixed-Point Accumulation

A Nonvolatile AI-Edge Processor With Lossless-Compressed-Computing STT-MRAM Near-Memory-Compute Macro Using Dynamic Floating-/Fixed-Point Accumulation 150 150
Author(s): De-Qi You, Win-San Khwa, Bo Zhang, Jui-Jen Wu, Chuan-Jia Jhang, Guan-Yi Lin, Po-Jung Chen, Ting-Chien Chiu, Fang-Yi Chen, Andrew Lee, Yu-Cheng Hung, Chung-Chuan Lo, Ren-Shuo Liu, Chih-Cheng Hsieh, Kea-Tiong Tang, Yu-Der Chih, Tsung-Yung Jonathan Chang, Meng-Fan Chang

Abstract:

Nonvolatile AI-edge processors based on near-memory-compute (nvNMC) enable energy-efficient multiply-and-accumulate (MAC) operations with short wakeup latency for edge inference operations. Lossless compression is required for floating-point (FP) neural network (NN) models under on-chip memory capacity constraints; however, this imposes several challenges: 1) data decompression overhead due to lossless FP weight encoding; 2) …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3705432
Publisher: IEEE

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A 28-nm PVT Inner-Tracking Time-Domain Compute-In-Memory Macro for Edge-AI Devices

A 28-nm PVT Inner-Tracking Time-Domain Compute-In-Memory Macro for Edge-AI Devices 150 150
Author(s): Yuanzhe Zhao, Yuheng Wang, Heng Xie, Zijian Wang, Rui P. Martins, Chi-Hang Chan, Minglei Zhang

Abstract:

This article presents an energy-efficient and process-, voltage-, and temperature (PVT)-robust time-domain (TD) compute-in-memory (CIM) macro for edge artificial intelligence (AI) devices. It features: 1) a PVT inner-tracking (PIT) technique that aligns the PVT responses of TD computation and TD quantization, delivering inherent robustness without incurring extra power or circuit …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3691189
Publisher: IEEE

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